WO2003058396A2

WO2003058396A2 - Forecasted financial analysis planning and dispatching of distributed resources

Info

Publication number: WO2003058396A2
Application number: PCT/US2002/041390
Authority: WO
Inventors: Deia Salah-Eldin Bayoumi; Danny E. Julian; Edward M. Petrie; Aaron F. Snyder
Original assignee: Abb Research Ltd.
Priority date: 2001-12-28
Filing date: 2002-12-26
Publication date: 2003-07-17
Also published as: AU2002364915A1; WO2003058396A3; AU2002364915A8

Abstract

A tool for determining the feasibility of adding distributed resources to an existing facility by determining the cost/benefit ratio of using distributed resources for a particular project. The tool is integrated with database engines for processing data acquired from utilities rate tables (116), location defaults (118), distributed resources cost models (120) and distributed resources manufacture data (122). Fuel prices and electrical thermal energy prices and trades may be received from on-line sources or may be calculated based on projections of historical data (124). Artificial intelligence agents may recommend adjustments to project constraints and generate optimal distributed resources technology mix and use. Alternately, if historical data is provided, probabilistic techniques may generate a plurality of possible solutions. After a user confirms a desired solution, comprehensive reports and graphs (134), cost and financial solutions (140) may be provided.

Description

FORECASTED FINANCIAL ANALYSIS PLANNING AND

DISPATCHING OF DISTRIBUTED RESOURCES

FIELD OF THE INVENTION

This invention relates to the field of computing and in particular to the field of software tools for financial forecasting.

BACKGROUND

As computing was done in the 1960's with powerful, central computers connected to remote terminals, so traditionally electrical power has been produced by large centralized power stations that generate electricity and transmit the electricity over high-voltage transmission lines. The voltage is then stepped down in several stages and distributed to the customer. But just as the mainframes of the 1960's were superseded by the desktop PC, and then by networked computers and Web-based information systems, so electrical power distribution systems are evolving. This evolution is a result of drawbacks in the generation of power by large centralized power stations, of changes in the regulation of the electrical industry, and of technological advances in the development of different types of small power generators and storage devices. The bulk of today's electric power comes from central power plants, most of which use large, fossil-fired combination or nuclear boilers to produce steam that drives steam turbine generators. There are numerous disadvantages to these traditional power plants.

Most of these plants have outputs of more than 100 megawatts (MW), making them not only physically large but also complex in terms of the facilities they require. Site selection and procurement are often a real challenge because of this. Often no sites are available in the area in which the plant is needed, or ordinances are in effect (such as no high voltage power lines are permitted in certain areas) that make acquisition of an appropriate site difficult. There is considerable public resistance on aesthetic, health, and safety grounds, to building more large centralized power plants, especially nuclear and traditional fossil-fueled plants. High voltage transmission lines are very unpopular. People object to the building of large power plants on environmental grounds as well. Long distance electricity transmission via high voltage power lines has considerable environmental impact.

Long distance transmission of electricity is expensive, representing a major cost to the end-user because of the investment required in the infrastructure and because losses accrue in the long distance transmission of electricity proportionate to the distance traveled so that additional electricity must be generated over that needed to handle the power needs of the area.

Plant efficiency of older, existing large power plants is low. The plant efficiency of large central generation units can be in the 28-35% range, depending on the age of the plant. This means that the plant converts only between 28-35% of the energy in their fuel into useful electric power. To exacerbate the matter, typical large central plants must be over-designed to allow for future capacity, and consequently these large central plants run for most of their life in a very inefficient manner.

In areas where demand has expanded beyond the capacity of large power plants, upgrading of existing power plants may be required if the plant is to provide the needed additional power. This is often an expensive and inefficient process.

Some areas are too remote to receive electricity from existing transmission lines, thereby requiring extension of existing transmission lines which results in a corresponding increased cost for electric power.

In part due to concerns regarding centralized power production, the enactment of the Public Utility Regulatory Policies Act of 1978 (PURPA) encouraged the commercial use of decentralized, small-scale power production. PURPA' s primary objective was to encourage improvements in energy efficiency through the expanded use of co-generation and by creating a market for electricity produced from unconventional sources. The 1992 Federal Energy Policy Act served to enhance competition in the electric energy sector by providing open access to the United States' electricity transmission network, called the "grid."

Distributed power generation and storage could provide an alternative to the way utilities and consumers supply electricity which would enable electricity providers to minimize investment, improve reliability and efficiency, and lower costs. Distributed resources can enable the placement of energy generation and storage as close to the point of consumption as possible, with increased conversion efficiency and decreased environmental impact. Small plants can be installed quickly and can be built close to where the electric demand is greatest. In many cases, no additional transmission lines are needed. A distributed generation unit does not carry a high transmission and distribution cost burden because it can be sited close to where electricity is used, resulting in savings to the end-user.

New technologies concerning small-scale power generators and storage units also have been a force contributing to an impetus for change in the electrical power industry. A market for distributed power generation is developing. The Distributed Power Coalition of America estimates that small- scale projects could capture twenty percent of new generating capacity (35 Gigawatts) in the next twenty years. Distributed generation is any small-scale power generation technology that provides electric power at a site closer to customers than central station generation. The small-scale power generators may be interconnected to the distribution system (the grid) or may be connected directly to a customer's facilities. Technologies include gas turbines, photovoltaics, wind turbines, engine generators and fuel cells. These small (5 to 1,500 kilowatt) generators are now at the early commercial or field prototype stage. In addition to distributed generation, distributed resources include distributed storage systems such as the storage of energy by small-scale energy storage devices including batteries, superconducting magnetic energy storage (SMES), and flywheels. Efficiency of power production of the new small generators is far better than traditional existing power plants. In contrast to the 28-35% efficiency rate of older, centralized large power plants, efficiencies of 40 to 50% are attributed to small fuel cells and to various new gas turbines and combined cycle units suitable for distributed generation applications. For certain novel technologies, such as a fuel cell/gas turbine hybrid, electrical efficiencies of about 70% are claimed. Co-generation, providing both electricity and heat or cooling at the same time, improves the overall efficiency of the installation even further, up to 90%.

Project sponsors benefit by being able to use electric power generated by distributed resources to avoid high demand charges during peak periods and gain opportunities to profit from selling excess power to the grid. Utilities gain reliability benefits from the additional capacity generated by the distributed resources, and end-users are not burdened with the capital costs of additional generation. In some cases, electricity generated by distributed resources is less costly than electricity from a large centralized power plant.

Distributed power generation and storage could provide an alternative to the way end-users receive electricity which would enable electricity providers to minimize investment, improve reliability and efficiency, and lower costs. Distributed resources can enable the placement of energy generation and storage as close to the point of consumption as possible, with increased conversion efficiency and decreased environmental impact. Distributed power generation can also be used to supplement the existing grid, thereby improving power reliability.

Heretofore, however, no automated tools have been available to determine the costs and benefits of using distributed resources for a certain project. It would be helpful if there were a tool available that could forecast the feasibility of adding distributed resources to an existing facility.

SUMMARY OF THE INVENTION

Systems and methods for determining the feasibility of adding distributed resources to an existing facility by determining the cost/benefit ratio of using distributed resources for a particular project are disclosed. A tool is integrated with database engines for processing data acquired from utilities rate tables, location defaults, distributed resources cost models and distributed resources manufacture data, for example.

According to aspects of the invention, a tool receives fuel prices and electrical thermal energy prices and trades from on-line sources and artificial intelligence agents recommend adjustments to project constraints to obtain optimal distributed resources technology mix and use. A number of possible solutions may be generated. After a user confirms a desired solution, comprehensive reports and graphs, cost, and financial solutions may be provided. According to further aspects of the invention, current fuel prices and electrical thermal energy prices and trades are estimated based on historical fuel prices and electrical/thermal energy past prices and trades, and a processor employing probabilistic techniques recommends adjustments to the project constraints and the optimal distributed resources technology mix and use. After the customer confinns his selection, comprehensive reports and graphs, and cost and financial solutions for the project may be generated.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing summary, as well as the following detailed description of preferred embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the invention, there is shown in the drawings exemplary constructions of the invention; however, the invention is not limited to the specific methods and instrumentalities disclosed. In the drawings:

Figure 1 is a block diagram of an exemplary planning tool that determines costs and benefits of additional and existing distributed resources devices in accordance with the present invention;

Figure 2 is a block diagram of another exemplary planning tool in accordance with the present invention;

Figure 3 is a block diagram showing an exemplary computing environment in which aspects of the invention may be implemented; Figure 4 is a block diagram showing an exemplary network environment in which aspects of the invention may be implemented; Figure 5 is a graph of a 15 minute load profile without VU/DR;

Figure 6 is a graph of a 15 minute load profile with VU/DR;

Figure 7 is a graph of 15 minute peak energy values without VU/DR; and Figure 8 is a graph of 15 minute peak energy values without

VU/DR.

DETAILED DESCRIPTION OF THE INVENTION

Figures 1 and 2 are block diagrams of exemplary tools that determine the financial benefits and costs of using and adding to existing distributed resources in an electrical power system or within the user electrical network. A tool such as the disclosed financial planning tool may be used by a user who is interested in having a distributed generation (DG) project or virtual utility (VU) in the user's facility. A virtual utility is a microgrid typically comprising, for example, aggregated generation, combined heat and power plants, distribution, protection, control, metering and ancillary products and services operating in an automated fashion as a single power plant. Such a user may have an existing facility, for example, and be interested in increasing the capacity of the facility. In order to obtain results, the user inputs existing facility equipment data 110, existing distributed resources load information 108, project information 106, and constraints for the project 104 into a data collection module 112.

Existing facility data 110 includes information such as the non-DG power system equipment that is currently owned, for example. This information may include monthly charges associated with each piece of non-DG equipment owned, and other associated demand charges, credits, penalties, power quality costs, power quality credits and/or any other additional costs or credits.

With respect to load information 108, it is contemplated that according to an embodiment, a user is given a choice to include time-series data from a site by selecting a "load profile" option. Time-series data is a load profile of energy consumed in kWh and kVARh at evenly distributed time intervals from a site. Alternately, a user may choose a "no load profile" option, to omit this data. If such data is available, it is preferable that the user selects to include time-series data. If the "no load" option is chosen, an average percentage of energy consumption preferably is substituted for the load profile data. If a user load profile is available and the user has selected the load profile option, a user is preferably prompted for information data inputs for an existing site. The information for which the user may be prompted includes but is not limited to: load, load factor, monthly charge, monthly demand charge, monthly curtailment credits, monthly curtailment penalties, tax credits per year, other credits per year, other credits, other penalties per year, other penalties, reactive power penalties, reactive power credits, and power factor set-point. Preferably, not all of this information is required but typically the more information that is entered, the better analysis the processes of the invention can provide. Entering as much data as possible about the penalties and credits will lead the user to a more accurate cost/benefits determination. Existing user load information of kW demand and power factor is desirably entered as well. For the monthly average energy rate ($/kWh, dollars per kilowatt hour), a rate database with default location may be integrated into the tool. Preferably, when the user chooses the state where the project is located, an average rate is automatically entered. The user may override the average rate with another rate (e.g., an actual rate, if the average rate is inaccurate, or does not match what the user actually pays). The rate inputted by the user then replaces the default value.

If a user chooses the "no load" option, the user may be prompted for such information as load, load factor, monthly charge, average monthly energy rate, power factor, curtailment credits, curtailment penalties, tax credits, other credits, other penalties, reactive power penalties, reactive power credits, and power factor set point, for example.

Information concerning existing distributed resources 108 may also be provided. This information desirably includes, for example, the initial cost of purchasing the presently owned distributed resources devices, and the costs associated with shipping, installation, operational costs, land fee costs, and any other applicable costs of the distributed resource or resources. If the user chooses to include time-series data from a site by selecting the "load profile" option above, the user is preferably prompted for information concerning the distributed generation solution being purchased, or already existing on the user site. Preferably, operational costs of the distributed resource or resources to be installed or already existing on the user site are supplied. Credits that are allowable by the use and installation of a distributed resource are preferably entered as well. Technical data about the distributed resource, such as distributed resource size in kW, heat rate and power factor, are captured. Preferably, the inputs are independent from distributed generation technology, as the technology-related inputs, such as fuel price, are inputted in dollar-per-unit-of-energy output ($/kWh). In an embodiment, distributed resources are dispatched to shave the user peak load based on one of at least two conditions: a threshold utility energy rate ($/kWh) is reached, above which the distributed resource(s) is turned on to feed the user load and to reduce the utility bill, or a threshold load demand (kW) is reached, such that when the user demand goes above that value, the distributed resource(s) will be turned on to reduce the demand from the utility. In an embodiment, the distributed resource is assumed to be either on or off. An "on" value is represented by "100%" and an "off value is represented by "0%". Alternatively, values between 0% and 100% are used to represent some configurable percentage of full capacity.

Information requested from the user may include load (demand) to turn the distributed resource(s) on, rate to turn distributed resource(s) on, expected number of years of return of investment, distributed resource(s) initial cost (e.g., price and shipping and installation), energy storage cost, fuel storage cost, land fee cost, power quality problems cost, metering cost, and other fixed costs, for example. Furthermore, requested information may include tax credit per year, power quality savings per year, power quality credits, environmental credits and other credits, for example. The following distributed resource(s) operation/annual cost information may also be requested: distributed resource size, distributed resource fuel price, operating and maintenance costs, distributed resource power factor at rated load and heat rate, for example. Alternately, a user who chooses the "no-load" option may be prompted for (1) information concerning the expected percentage of time of distributed resource operation: percentage of time per year of running the distributed resource as a backup and peak shaving, percentage of time per year of the distributed resource shut-down time, heat rate, expected number of years of return of investment; (2) distributed resource fixed costs: distributed resource initial cost (price and shipping and installation), energy storage cost, fuel storage cost, land fee cost, power quality problems cost, metering cost, and other fixed costs; (3) distributed resource fixed credits: tax credit per year, power quality savings per year, power quality credits, environmental credits and other credits; and (4) distributed resource operation/annual cost information: distributed resource size, distributed resource fuel price, operating and maintenance costs, distributed resource power factor at rated load and heat rate.

Project information 106 may include information concerning the subject of the project. Such information may include the reason the user is considering adding distributed generation units. For example, the reason may be because the user wants to increase the load that can be generated by the facility. Similarly, the user may want to add another line to the lines that presently exist. The user may want to add another city to the locations to which the facility provides electricity. One or more reason may be entered into project information 106. The user may be prompted for information such as, but not limited to, description, user, telephone number, fax number, notes, address, e-mail address, type of facility, energy source, notes, reference site, location of reference site, and information concerning existing distributed generation units, such as manufacturer, rated output of distributed generation unit, model number of distributed generation unit, and number of units.

The user enters any applicable constraints 104 for the project. Such constraints may originate from the user (e.g., the user's budget has approved a certain amount to invest in the project), from the municipality (e.g., the facility may be limited to certain emissions, or a certain type of distributed generation unit may not be permitted because of environmental concerns) or from any other source (e.g., photovoltaic cells are not feasible because the area does not receive enough clear weather to make the use of photovoltaic cells feasible).

Inputs are received by a data collection module 112 that validates that the minimum amount of data has been entered to perform the determinations. For example, data collection module 112 may determine that the number of years for return of investment has not been entered and as this is required information in an embodiment of the invention, the module 112 prompts the user to enter this information. Data collection module 112 also converts the data into a format acceptable by a module 114 (in an exemplary embodiment) or module 214 (in another exemplary embodiment) that processes this data. In certain embodiments, the 15, 30 and 60-minute profiles preferably have 100 columns including two unused fields, a data field, a type field, and a field for every 15 minutes for 24 hours. The profiles in certain embodiments preferably have 733 rows, including 3 header rows and 730 rows of data, i.e., two rows per day, one for kWh and one for kVARh. For the other profiles, it is contemplated that five columns may be used as there is only one kWh and one kVARh point per period. The number of rows desirably may vary from 730 to 2 depending on the time series. Preferably, these files are in comma-separated format, and more preferably are in a spreadsheet format, such as in MICROSOFT EXCEL format, but it should be understood that any suitable format is included within the scope of the invention.

The tool preferably has one or more built-in database engines such as an engine for utility rate tables 116, wliich are based on the user and the location and are used in calculating the electricity bill which may provide, for example, data concerning interconnection charges, load profile for different user categories, etc. For example, user data may be entered onto a spreadsheet such as, but not limited to, an EXCEL spreadsheet. Such a spreadsheet typically creates a database accessible by software in which the desired determinations are performed.

Another category of information may be location data 118. Location may impact the results because, for example, one location may only allow a certain type of unit to run for a certain period of time. Similarly, a given unit may run at a given efficiency based on altitude and thus, for example, the same unit may run at 40% efficiency in Colorado but 45 % efficiency in Florida. Similarly, different states may have different emission requirements and may restrict a given unit to a certain amount of operating time. Receiving this data from an automated source enables the user-provided inputs to be minimized. Distributed resources cost models 120 is a mathematical model that provides information such as, for example, for a particular model of machine, for the length of time the machine is run, and for the amount of fuel put in the machine, the cost to produce the energy generated by the machine. Manufacturer data 122 includes information such as, for example, how many hours a unit can be run before maintenance is required, how many times a unit can be run before a unit needs to be replaced, and how many times a unit can be started or stopped per day, as typically, certain distributed units require some period of time to warm up and some period of time to cool down before reuse. In an embodiment, the distributed resources fuel prices 124 and electrical thermal energy prices and trades 126 are supplied by historical data, and in another embodiment, the distributed resources fuel prices 224 and electrical thermal energy prices and trades 226 are provided by on-line sources 242 and 244, respectively, for example.

Preferably, on-line sources 242, 244 provide current information from Internet sources. Multiple artificial intelligence (Al) agents 214 including neural networks (responsible for pattern recognition), fuzzy logic (responsible for control schemes) and genetic algorithms (responsible for the optimization process) may be employed, for example.

Alternatively, probabilistic techniques module 114 receives historical data for fuel prices and electrical/thermal prices, preferably based on three to five years of data. Forecasts are then run, based on the historical data in order to estimate a current price based on what happened in the past.

Probabilistic techniques module 114 preferably includes the development of efficient (randomized) processes, the modeling of uncertainty in reactive systems, the quantification of system properties, and the evaluation of performance and reliability of systems. Probabilistic techniques module 114 is desirable when critical parameters are not known with certainty. Probabilistic techniques module 114 may be used in process/cost model development, identification of input parameters of importance and output figures of merit, quantification of input uncertainty distributions, probabilistic simulation using personal computer based Monte Carlo techniques, and interpretation summarization of results. Using probabilistic techniques module 114, technology insights may be used to elicit and encode uncertain variables. Using structured interview techniques, preferably the uncertainty of process/cost parameters can be characterized with a minimum of bias and a maximization of expert knowledge. Probabilistic techniques module 114 may employ the use of probabilistic networks to compactly represent a distribution over a set of random variables.

The inputs are collected and validated and are passed to a module that uses probabilistic techniques 114 (in one embodiment) or to multiple Al agents 214 (in another embodiment) to recommend any adjustments 128 to project constraints and return one or more solutions that optimize the mix and use of distributed resources 130. For example, multiple Al agents 214 may return a solution that pollutes the environment more and violates the budget but provides the best operation costs. A second solution may not violate any of the constraints but may be associated with higher costs and may require the addition of one or more new DR technologies 130. The user can modify the constraints 132 in light of the solution results in order to obtain a desired solution. Alternatively, instead of modifying constraints, the user may provide a set of rules by which a decision can be made.

The tool then preferably provides a complete cost and financial analysis for the chosen solution in the form of reports and graphs 134, and savings on the utility bill as well as revenues from selling energy back to the utility 136. Savings and revenue output 136 in certain embodiments is preferably displayed on a screen, and includes values including, for example, annual electricity bill connected to utility only, annual electricity bill connected to utility and DR, annual savings on own loads, virtual utility benefits, such as, for example, energy trading revenues, savings on interruptions, and financial solutions, such as, for example, load, monthly payment, monthly payment on interest, monthly payment on principle, future value, present value, and net present value. The user may adjust the rate tables and peak on/off times in constraints 132. The determinations are updated and updated values will be displayed on the savings and revenue output screen.

Preferably, the utility bill without virtual utility is calculated using equation (1):

Utility bill = energy charge + other charges + penalties - credits (1)

Preferably the utility bill with the virtual utility is calculated using equation (2):

Utility bill = [energy charge - energy supplied by DG] + [other charges + charges added by DG installation/use] + [penalties + penalties incurred by installation/use of DG] - [credits + added credits by DG] (2)

In the "load profile" path, a tier-rate table preferably is inputted by the user and the peak time of use is defined. The energy charge is calculated point-by-point from the user load profile by using equation (3):

Energy charge = energy usage * appropriate rate (3)

The process preferably acquires the appropriate rate by determining if the energy is being consumed at on-peak or off-peak times, and ascertaining the correct price tier to which the consumption belongs. If the reactive energy is inputted as part of the user load profile, then average prices for the kVARh penalty/credit, as well as a power factor cut-off value, are preferably entered as well. This cut-off value preferably is the power factor allowed by the utility without incurring any additional charges or credits. The process preferably determines which price to use based on the following set of cases: Case (1): when lagging reactive energy is less than the power factor set point, a penalty preferably will be applied, as follows:

Reactive Energy Price ($) = Reactive Energy (kVARh) x Penalty Rate ($/kVARh)

Case (2): when lagging reactive energy is greater than or equal to the power factor set point, then preferably neither penalty nor credit will be applied:

Reactive Energy Price ($) = 0

Case (3): when leading reactive energy is less than the power factor set point, a penalty preferably will be applied, as follows:

Reactive. Energy Price ($) = Reactive Energy (kVARh) x Penalty Rate

($/kVARh)

Case (4): when leading reactive energy is greater than or equal to the power factor set point, then a credit preferably will be applied, as follows:

Reactive Energy Credit ($) = Reactive Energy (kVARh) x Credit Rate ($/kVARh).

The process preferably distinguishes between the lagging and leading reactive energy by the sign of the power factor inputted. Negative values for the power factor indicate a leading power factor and hence a leading reactive energy, and vice versa.

The distributed resource preferably is dispatched by one of two triggers, either the energy rates are higher than a preset value or load demand is higher than a preset value. When the distributed resource(s) is dispatched, in certain embodiments the distributed resource(s) is assumed to run at substantially

100% of its capacity. Alternatively, the distributed resource may be assumed to run at some configurable percentage of full capacity represented by a value between 0% and 100%. The energy produced by the distributed resource (both active and reactive in kWh and kVARh, respectively) preferably adjusts the user load profile. After the modified user load profile is determined, new energy costs are determined. The utility bill preferably is re-calculated to reflect savings from shaving the load and additional/savings from reactive power supplied by the distributed resource or resources using equation (4):

DG kWh =DG size (kW) x load factor x appropriate time factor (4)

where DG = distributed generation unit, and the load factor is the ratio of actual total usage to the amount that would have been used if the user consumed energy uniformly during the day at the rate of maximum demand. Preferably, if the user has load profile data, case load factor is calculated. Alternatively, if only basic information such as utility bill is available, the load factor is preferably used as an input, because the available information is insufficient to perform load factor calculations.

The load factor is preferably determined according to equation (5):

Load Factor = Total Usage kWh / (24 * Peak Usage kW) (5)

The appropriate time factor varies with the load-profile data-points type. Table 1 shows the desired factors that are used with different load profiles:

Table 1 : Load-Profile Time Factors The DG reactive energy preferably is calculated using equation (6):

DG kVARh = DG kWh x TAN (DG power factor angle) (6)

where DG power factor angle = TAN-1 (DG power factor value).

The electrical energy produced by the DG will modify the user load profile by subtracting the energy produced by the DG from the energy consumed from the utility. The reactive power will be subtracted or added to the original user profile values depending on being leading or lagging respectively.

The above described pricing techniques for both active (kWh) and reactive (kVARh) energies preferably are reapplied to calculate the new costs with VU.

Thermal energy produced by the DG (in Btu) preferably is also calculated and reported as being available. The process preferably uses equation (7) for the time the DG is being dispatched:

Available Thermal Energy (Btu) = Heat Rate (Btu/kWh) x Total Energy Produced by the DG (kWh) (7)

When the user has selected the "no load profile" option, the following processes apply. The user load in kW is preferably inputted with a load factor. An average price for the energy preferably is defined by the user location

(based on national industrial averages for 1998, for example) or may be inputted by the user. The energy price preferably is determined using equation (8):

Energy charge ($) per year = User load (kW) x Load Factor x Average Monthly Rate x 12 (8)

The reactive energy is preferably determined using equation (9): Reactive Energy (kVARh) = User load (kW) x Load Factor x TAN (power factor angle) (9)

where power factor angle =TAN-1 (power factor value). The same pricing process used with the load profile path applies here using the following cases:

Case (1): when lagging reactive energy is less than the Power factor set point, then a penalty preferably will be applied:

Reactive Energy Price ($) = Reactive Energy (kVARh) x Penalty Rate ($/kVARh)

Case (2): when lagging reactive energy is greater than or equal to the Power factor set point, then neither penalty nor credit will be applied:

Reactive Energy Price ($) = 0

Case (3): when leading reactive energy is less than the power factor set point, then a penalty preferably will be applied:

Reactive Energy Price ($) = Reactive Energy (kVARh) x Penalty Rate ($/kVARh)

Case (4): when leading reactive energy is greater than or equal to the power factor set point, then a credit will be applied:

Reactive Energy Credit ($) = Reactive Energy (kVARh) x Credit Rate ($/kVARh)

There is preferably a distinction between the lagging and leading reactive energy by the sign of the power factor inputted. Negative values for the power factor indicate a leading power factor and hence a leading reactive energy, and vice versa.

Preferably, the user inputs the DG percentage of running time per year for peak-shaving and backup. This value preferably is used to calculate how much energy DG will supply for the user's own loads per year, in accordance with equation (10):

DG kWh per year =DG size (kW) x Load factor x % of running time per year (10)

where load factor is the ratio of actual total usage to the amount that would have been used if the user consumed energy uniformly during the day at the rate of maximum demand.

If insufficient information is available to calculate load factor, preferably, load factor can be input by a user. Alternatively, load factor can be calculated using equation (11):

Load Factor = Total Usage kWh / (24 * Peak Usage kW) (11)

The DG reactive energy is preferably calculated using equation

(12):

DG kVARh per year = DG kWh per year x TAN (DG power factor angle)

(12) where DG power factor angle =TAN-1 (DG power factor value).

The electrical energy produced by the DG modifies the user load profile by subtracting the energy produced by the DG from the energy consumed from the utility. The reactive power is subtracted or added to the original user profile values depending on being leading or lagging respectively. The above pricing techniques for both active (kWh) and reactive

(kVARh) energies are preferably reapplied to calculate the new costs with VU. Thermal energy produced by the DG (in Btu) preferably is determined also and reported as being available, using equation (13):

Available Thermal Energy (Btu) per year = Heat Rate (Btu/kWh) x Total Energy Produced by the DG (kWh) per year (13)

The optimal use and mix of distributed resources 138 is also provided and may include the times that each unit should be operated and percentages of mix between different technologies with several options. The Al agents, according to certain embodiments, can process numerous changes of scenarios and accept real-time data from on-line resources. Deciding which option to select may be done by user input or by referring to a predefined set of rules and constraints. The Al for future analysis produces comprehensive reports and graphs 134 that are desirably customizable to meet the users needs and desires.

Hence, a system and method in accordance with the present invention produces a financial analysis of distributed resources in electrical power systems and a dispatching plan of distributed resources based on economic factors. An optimal mix and use of distributed resources technologies is offered. In an embodiment of the invention, multiple Al agents offer more than one optimal solution 140 to chose from. The tool is user-interactive by offering several adjustments to project constraints and different distributed resources technologies. Using minimal input from the user, the tool can offer an optimal solution 140 by assuming many default values from the several database engines. The tool produces reports and graphs and a novel technique is employed to produce the results.

Justification of any virtual utility (VU) project will derive from its benefit versus the cost of serving the load. Under the restructured regulatory environment, electricity costs are certainly expected to change, though whether to the benefit of the consumer or the provider remains to be determined. Aside from this uncertainty, evidence of a large amount of volatility in the electricity market also drives the use of distributed resources (DR). As a hedge against price spikes, DR is expected to provide a large benefit.

To calculate the viability of a DR project, a full cost-benefit analysis (CBA) may be performed. The CBA preferably is detailed enough to provide the planner with a selectable choice, but not so detailed that an inordinate amount of time is needed to calculate the results. This balance of detail versus time is crucial to a DR project trying to compete on price alone.

Detailed models of DR sources (turbines, combustion engines/turbines, photovoltaics, wind generators, etc.) are available from the manufacturers of such devices. From a data entry standpoint, the collection is typically the least complex portion, with selection of the proper data to retain and the use of the data being more difficult tasks.

For other costs, the data collection may be the more difficult task, as there are more than 50 regulatory bodies to consult for data such as intercomiection standards and costs, tariff structures, land use costs, environmental costs, and the like. Woven into the problem is the issue of transparency, with these costs being set by the regulatory bodies, but somewhat open to negotiation. A large energy provider has the political clout to request changes in the regulated costs and return on investment allowed, where a new player in the DG market will have practically none.

The biggest unknown is the fuel cost: volatility in the petroleum and natural gas markets creates large swings in the final cost of electricity. This co-reliance can act to strengthen the position of a large generator or eliminate them entirely from competition. To sum up, the financial analysis of a DR project is made up of many factors, regulated, competitive, and unstable. A software program preferably will contain enough data to use this data in a coherent manner, without an extraordinary amount of time to complete the analysis required.

It is desirable to model DR sources in detail beyond the simplified kW/kVAR and negative load representations. Examples of DR sources include but are not limited to: diesel generators, natural gas reciprocating engines, micro- turbines, thermal-solar plants, photovoltaic modules, wind turbines, batteries, and fuel cells. The most flexible implementation preferably includes the ability to model any new device that may be installed.

For reciprocating engine generators, desired data includes rated power, minimum allowed power, no-load fuel consumption, full-load fuel consumption, capital cost (device, overhaul, operation and maintenance), overhaul period, operational lifetime, and fuel price.

The data desired for photovoltaic (PV) modules preferably includes, for example, the clearness index of the site, the latitude, the daily (or essentially an average) radiation or insolation, the module operating temperature, the short circuit current, the open circuit voltage, the maximum power point voltage, the maximum power point current, the number of cells in series, the number of cells in parallel, the module area, the current temperature coefficient, the voltage temperature coefficient, the ambient temperature of the site, the array efficiency, the capital cost (module rack, tracking module, rectifier, inverter, installation), the operational lifetime, the type of tracking, and the array slope.

Wind turbine data typically includes rated power, hub height, average interval for power, capital cost (tower, installation, overhaul, operation, maintenance, etc.), the overhaul time period, the average wind speed, the wind power scaling factor, the wind turbine spacing, the wind power response, the Weibull coefficient, the diurnal pattern strength, and the hour of peak wind speed, for example.

Batteries models are typically dependent on the constant current discharge rate of each type of battery, the beginning (e.g., 20% charged) and end (e.g., 80% charged) of the charging cycle voltages, the depth of discharge versus cycles to failure curve, the cycle life, the float life, the round trip efficiency, the minimum state of charge, the charge rate, nominal voltage, nominal capacity, capacity ratio, rate constant, capital costs (device and operation and maintenance), and the internal resistance, for example.

Fuel cells are typically classified by output power (continuous and peak), and capital costs (device, inverter, fuel, water, operation and maintenance). Data such as rated power, minimum allowed fuel consumption, capital cost (device, fuel, overhaul, operation, and maintenance, etc.), operational lifetime, and fuel price is preferably acquired for micro-turbines.

If the DR is expected to interact with the electric grid in any manner, there are interconnection charges such as protective devices, net meter costs, substation maintenance, transformer costs, communication costs and feasibility study costs.

The data for existing service preferably includes the actual cost of the electricity delivered, on a state-by-state basis, with the tariff schedules that are publicly available. Entries for service fees, communications costs, billing costs, and such are also preferably included.

If the DR were to be placed on a non-owned site, land use fees typically apply and are preferably included in the calculations.

Any type of source fuel price is preferably part of the CBA, including diesel fuel, natural gas, gasoline, and propane. Figures for quantity use, stored amount, availability, and sureness of supply are preferably included.

Operation and maintenance costs can be on a price per unit of energy basis, price per unit of time basis, price per service basis, and emergency trip basis. All are preferably included, along with probabilities of payment (reliability data) into the financial analysis. The cost of communication is desirably included, whether fixed land-line, microwave, fiber-optic or other technology. Probability of failure should be included to ensure that adequate communication structures are constructed to assure the performance of the DR under the operating conditions (e.g., normal, stressed, emergency, outage). Two-way communication is preferable under the VU paradigm, which will influence cost via redundancy of circuits. Power quality issues such as voltage sags (or dips) and harmonics

(from switching or power electronics operations) form another portion of a desirable power system analysis. The cost of poor power delivery preferably is accounted for, as well as the cost of voltage support devices such as capacitor banks, protective relays, and harmonic filters, if desired. The benefit of serving as a peak-shaving device preferably is desirably included in the financial analysis, either from an avoided cost standpoint or a delivery of service standpoint.

A traditional meter, such as a meter on a residence, measures the amount of electrical power consumed. A bi-directional meter that measures power consumed and power added to the grid, is preferable when power can also be added to the grid. A bi-directional meter is more expensive than a one-way meter and this cost and whatever communications are desired and preferably are taken into account.

The costs of meeting environmental targets, whether negative in the form of fines or positive in the form of emissions credits, will preferably be included in the added- value portion of the DR financial analysis. The cost of serving this would preferably include any incentives (renewable energy, efficiency, etc.), the actual tax rate, and the depreciation model assumed for the initial cost. If the initial capital costs are provided by loans, the interest rate, the load period, and the down payment fraction are all desired data and are preferably included in the calculations.

Data for the minimum load allowed, the maximum load allowed, the average load, the amount of load that is deferrable, the deferral period, and the duty cycle are desirable for the analysis. Miscellaneous costs might include, but are not limited to, additional equipment, distribution enhancement, installation overhead, import tariffs, shipping, administration, and equipment salvage value (negative cost).

The financial analysis tool described above is intended to do a prompt and brief screening of the costs/benefits of a Virtual Utility system installation at a particular user site. Preferably, the program can be developed in the MICROSOFT EXCEL environment, with added Visual Basic for Applications (VBA) code and controls. While the particular spreadsheet or other software functionality is retained for user convenience, it is contemplated that the VUFA program uses its own specific VBA controls to simplify navigation and to facilitate the flow of information to and from the determination engine techniques. Illustrative Computing Environment

Figure 3 depicts an exemplary computing system 600 in accordance with the invention. Computing system 600 executes an exemplary computing application 680a capable of controlling and managing a group of distributed resources so that the management of distributed resources is optimized in accordance with the invention. Exemplary computing system 600 is controlled primarily by computer-readable instructions, which may be in the form of software, wherever, or by whatever means such software is stored or accessed. Such software may be executed within central processing unit (CPU) 610 to cause data processing system 600 to do work. In many known workstations and personal computers, central processing unit 610 is implemented by a single-chip CPU called a microprocessor. Coprocessor 615 is an optional processor, distinct from main CPU 610, that performs additional functions or assists CPU 610. One common type of coprocessor is the floating-point coprocessor, also called a numeric or math coprocessor, wliich is designed to perform numeric calculations faster and better than a general-purpose CPU 610. Recently, however, the functions of many coprocessors have been incorporated into more powerful single-chip microprocessors. In operation, CPU 610 fetches, decodes, and executes instructions, and transfers information to and from other resources via the computer's main data-transfer path, system bus 605. Such a system bus connects the components in computing system 600 and defines the medium for data exchange. System bus 605 typically includes data lines for sending data, address lines for sending addresses, and control lines for sending interrupts and for operating the system bus. An example of such a system bus is the PCI (Peripheral Component Interconnect) bus. Some of today's advanced busses provide a function called bus arbitration that regulates access to the bus by extension cards, controllers, and CPU 610. Devices that attach to these busses and arbitrate to take over the bus are called bus masters. Bus master support also allows multiprocessor configurations of the busses to be created by the addition of bus master adapters containing a processor and its support chips.

Memory devices coupled to system bus 605 include random access memory (RAM) 625 and read only memory (ROM) 630. Such memories include circuitry that allow information to be stored and retrieved. ROMs 630 generally contain stored data that cannot be modified. Data stored in RAM 625 can be read or changed by CPU 610 or other hardware devices. Access to RAM 625 and/or ROM 630 may be controlled by memory controller 620. Memory controller 620 may provide an address translation function that translates virtual addresses into physical addresses as instructions are executed. Memory controller 620 also may provide a memory protection function that isolates processes within the system and isolates system processes from user processes. Thus, a program running in user mode can access only memory mapped by its own process virtual address space; it cannot access memory within another process's virtual address space unless memory sharing between the processes has been set up.

In addition, computing system 600 may contain peripherals controller 635 responsible for communicating instructions from CPU 610 to peripherals, such as, printer 640, keyboard 645, mouse 650, and disk drive 655.

Display 665, which is controlled by display controller 663, is used to display visual output generated by computing system 600. Such visual output may include text, graphics, animated graphics, and video. Display 665 may be implemented with a CRT-based video display, an LCD-based flat-panel display, gas plasma-based flat-panel display, or a touch-panel. Display controller 663 includes electronic components required to generate a video signal that is sent to display 665.

Further, computing system 600 may contain network adaptor 670 which may be used to connect computing system 600 to an external communication network 310. Communications network 310 may provide computer users with means of communicating and transferring software and information electronically. Additionally, communications network 310 may provide distributed processing, which involves several computers and the sharing of workloads or cooperative efforts in performing a task. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used.

The computer described with respect to Figure 3 can be deployed as part of a computer network. In general, the above description applies to both server computers and client computers deployed in a network environment. Figure 4 illustrates an exemplary network environment 700, with a server computers 10a, 10b in communication with client computers 20a, 20b, 20c via a communications network 310, in which the present invention may be employed.

As shown in Figure 4, a number of servers 10a, 10b, etc., are interconnected via a communications network 310 (which may be a LAN, WAN, intranet or the Internet) with a number of client computers 20a, 20b, 20c, or computing devices, such as, mobile phone 15 and personal digital assistant 17. In a network environment in which communications network 310 is the Internet, for example, servers 10 can be Web servers with which clients 20 communicate via any of a number of known protocols, such as, hypertext transfer protocol (HTTP) or wireless application protocol (WAP), as well as other innovative communication protocols. Each client computer 20 can be equipped with computing application 680a to gain access to servers 10. Similarly, personal digital assistant 17 can be equipped with computing application 680b and mobile phone 15 can be equipped with computing application 680c to display and receive various data.

Thus, the present invention can be utilized in a computer network environment having client computing devices for accessing and interacting with the network and a server computer for interacting with client computers. However, the systems and methods of the present invention can be implemented with a variety of network-based architectures, and thus should not be limited to the example shown.

An exemplary case is illustrated below with respect to Tables 2-5. The information concerning the existing utility connection, the DR parameters, the virtual utility cost, and some financial values are shown in the following tables. Parameters include the load is 1900kW, the utility connection is 1700kW, and the DR is 230 kW. From that, a yearly load profile at 15-minute intervals is applied to get the cost and energy values.

Table 2: DR Data Input

Table 5: Financing Information Inputs

A rate tier (Table 6) determines how the DR is dispatched to alleviate the cost of the utility connection and is interpreted as follows: if the load in kWh is 250 or below and the cost of the DR is below the on-peak and/or off- peak value, the DR is turned on. Otherwise, the utility comiection is considered to best meet the load.

Table 6: Rate Tier Structure

Table 7 below and the graphs provided in Figures 5-8 illustrate the savings involved with the DR dispatched to meet the load within the restrictions given by the rate tiers, existing service parameters, and DR parameters.

Table 7: Financial Analysis Solution

Examples of benefits of the DR can be seen in Figures 5-8. Figure 5 is a graph of a 15 minute load profile without VU/DR and Figure 6 is a graph of a 15 minute load profile with VU/DR. As shown, for example, the daily average load profile has shifted to about 300kWh at 10:30am (Figure 6) from about 500kWh at the same time (Figure 5). Figure 7 is a graph of 15 minute peak energy values without VU/DR, and Figure 8 is a graph of 15 minute peak energy values with VU/DR. In Figure 7, the peak energy value is about lOOOkWh. This value is reduced to about 590kWh as shown in Figure 8.

Although illustrated and described herein with reference to certain specific embodiments, the present invention is nevertheless not intended to be limited to the details shown. Rather, various modifications may be made in the details within the scope and range of equivalents of the claims without departing from the invention.

Claims

WHAT IS CLAIMED IS:

1. A method for determining the feasibility of adding distributed resources to a facility for use in a project, comprising: receiving data related to the facility, the distributed resources, the project, and the project constraints; determining the costs and benefits of using the distributed resources based on the received data; and outputting the costs and benefits.

2. The method of claim 1 , wherein determining the costs and benefits comprises providing the data to a processor and using probabilistic analysis on the data.

3. The method of claim 1, wherein determining the costs and benefits comprises providing the data to artificial intelligence agents which then analyzed the data.

4. The method of claim 1, further comprising providing at least one recommended adjustment to the project constraints based on the determined costs and benefits.

5. The method of claim 4, further comprising determining the recommended adjustment responsive to obtaining optimal distributed resources.

6. The method of claim 4, further comprising receiving a selected recommended adjustment to the project constraints, adjusting the project constraints based on the selected recommended adjustment, and determining the costs and benefits of using the distributed resources based on the adjusted project constraints.

7. The method of claim 1, further comprising generating a report based on the costs and benefits.

8. The method of claim 1, further comprising validating that adequate data has been received for further processing prior to determining the costs and benefits of using the distributed resources based on the received data.

9. The method of claim 1, further comprising: generating a number of prospective distributed resources solutions; outputting the prospective distributed resources solutions; receiving a selected solution; and generating a report based on the selected solution.

10. The method of claim 1, wherein the received data further comprises fuel prices and electrical thermal energy prices, further comprising receiving the fuel prices and electrical thermal energy prices by at least one of estimating the prices based on historical data and retrieving the prices from an on-line data source.

11. A system for determining the feasibility of adding distributed resources to a facility for use in a project, comprising: a data collection module that receives data related to the facility, the distributed resources, the project, and the project constraints; and a processor that receives the data from the data collection module and determines the costs and benefits of using the distributed resources based on the received data and outputs the costs and benefits.

12. The system of claim 11, further comprising a display that displays the determined costs and benefits.

13. The system of claim 11 , wherein the data collection module validates that adequate data has been received for further processing prior to determining the costs and benefits of using the distributed resources based on the received data.

14. The system of claim 11 , wherein the processor uses probabilistic analysis on the data in determining the costs and benefits.

15. The system of claim 11 , wherein the processor comprises artificial intelligence agents in determining the costs and benefits.

16. The system of claim 11, further comprising a memory device for storing the data.

17. The system of claim 16, wherein the memory device comprises at least one database.

18. The system of claim 11 , wherein the processor provides at least one recommended adjustment to the project constraints based on the determined costs and benefits.

19. The system of claim 18, further comprising an input device for receiving a selected recommended adjustment to the project constraints, and the processor adjusts the project constraints based on the selected recommended adjustment, and determines the costs and benefits of using the distributed resources based on the adjusted project constraints.

20. The system of claim 11 , wherein the processor generates a report based on the costs and benefits.

21. The system of claim 11, further comprising a display and an input device, wherein: the processor generates a number of prospective distributed resources solutions; the display displays the prospective distributed resources solutions; the input device receives a selected solution; and the processor generates a report based on the selected solution.

22. The system of claim 11 , wherein the received data further comprises fuel prices and electrical thermal energy prices determined by at least one of estimating the prices based on historical data and retrieving the prices from an online data source.

23. A method of generating an optimized solution for a configuration of electrical power generation units, the method comprising: receiving information associated with existing non-distributed generation electrical power generation units of a facility; receiving information associated with existing distributed generation electrical power generation units of the facility; receiving information associated with at least one of a plurality of obj ectives of the proj ect; receiving information associated with constraints of the project; validating said information associated with existing non-distributed generation electrical power generation units of a facility, said information associated with existing distributed generation electrical power generation units of the facility; said information associated with at least one of a plurality of objectives of the project; said information associated with constraints of the project; receiving information associated with cost modeling and utility rates; and generating recommended constraint adjustments to provide an optimized solution for the configuration of electrical power generation units.