WO2003058396A2 - Planification d'analyse financiere previsionnelle et repartition de ressources distribuees - Google Patents

Planification d'analyse financiere previsionnelle et repartition de ressources distribuees Download PDF

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Publication number
WO2003058396A2
WO2003058396A2 PCT/US2002/041390 US0241390W WO03058396A2 WO 2003058396 A2 WO2003058396 A2 WO 2003058396A2 US 0241390 W US0241390 W US 0241390W WO 03058396 A2 WO03058396 A2 WO 03058396A2
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Prior art keywords
data
costs
distributed resources
benefits
project
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PCT/US2002/041390
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English (en)
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WO2003058396A3 (fr
Inventor
Deia Salah-Eldin Bayoumi
Danny E. Julian
Edward M. Petrie
Aaron F. Snyder
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Abb Research Ltd.
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Priority to AU2002364915A priority Critical patent/AU2002364915A1/en
Publication of WO2003058396A2 publication Critical patent/WO2003058396A2/fr
Publication of WO2003058396A3 publication Critical patent/WO2003058396A3/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Definitions

  • This invention relates to the field of computing and in particular to the field of software tools for financial forecasting.
  • 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.
  • 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.
  • PURPA Public Utility Regulatory Policies Act of 1978
  • 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.
  • 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.
  • SMES superconducting magnetic energy storage
  • Efficiency of power production of the new small generators is far better than traditional existing 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.
  • 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.
  • 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.
  • 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.
  • comprehensive reports and graphs, cost, and financial solutions may be provided.
  • 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.
  • 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
  • FIG. 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
  • FIGS 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.
  • 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.
  • Time-series data is a load profile of energy consumed in kWh and kVARh at evenly distributed time intervals from a site.
  • 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.
  • 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.
  • 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.
  • a rate database with default location may be integrated into the tool.
  • 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.
  • 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.
  • 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 ($/kWh).
  • distributed resources are dispatched to shave the user peak load based on one of at least two conditions: a threshold utility energy rate ($/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.
  • 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%”.
  • 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.
  • 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.
  • 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.
  • 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.
  • 730 rows 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.
  • 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.
  • user data may be entered onto a spreadsheet such as, but not limited to, an EXCEL spreadsheet.
  • a spreadsheet typically creates a database accessible by software in which the desired determinations are performed.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the utility bill without virtual utility is calculated using equation (1):
  • Utility bill energy charge + other charges + penalties - credits (1)
  • 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)
  • 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):
  • 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)
  • Reactive Energy Credit (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.
  • the distributed resource(s) is assumed to run at substantially
  • 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 distributed generation unit
  • 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.
  • case load factor is calculated.
  • 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 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.
  • 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:
  • 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
  • the energy price preferably is determined using equation (8):
  • Reactive Energy Price ($) Reactive Energy (kVARh) x Penalty Rate ($/kVARh)
  • Reactive Energy Price ($) Reactive Energy (kVARh) x Penalty Rate ($/kVARh)
  • Reactive Energy Credit (kVARh) x Credit Rate ($/kVARh)
  • 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):
  • 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.
  • load factor can be input by a user.
  • load factor can be calculated using equation (11):
  • the DG reactive energy is preferably calculated using equation
  • 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 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 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.
  • 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.
  • 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.
  • VU virtual utility
  • CBA cost-benefit analysis
  • DR sources turbines, combustion engines/turbines, photovoltaics, wind generators, etc.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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
  • 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 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.
  • 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.
  • 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.
  • FIG. 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.
  • CPU central processing unit
  • 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.
  • 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.
  • PCI Peripheral Component Interconnect
  • 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.
  • RAM random access memory
  • ROM read only memory
  • 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
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • HTTP hypertext transfer protocol
  • WAP wireless application protocol
  • Each client computer 20 can be equipped with computing application 680a to gain access to servers 10.
  • 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.
  • 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.
  • client computing devices for accessing and interacting with the network
  • server computer for interacting with client computers.
  • 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.
  • a rate tier 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 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.
  • 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.
  • 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
  • Figure 8 is a graph of 15 minute peak energy values with VU/DR.
  • the peak energy value is about lOOOkWh. This value is reduced to about 590kWh as shown in Figure 8.

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Abstract

L'invention concerne un outil permettant de déterminer la faisabilité d'une adjonction de ressources distribuées à une installation existante par détermination du rapport coût/profit d'utilisation de ressources distribuées pour un projet particulier. Ledit outil est intégré dans des moteurs de bases de données afin de traiter des données provenant de tables de débit de centrales électriques, de défauts d'implantation, de modèles de coût et de données de production de ressources distribuées. Les prix des carburants, de l'énergie thermoélectrique et les échanges commerciaux peuvent provenir de sources en ligne ou peuvent être calculés en fonction de projections de données historiques. Des agents d'intelligence artificielle peuvent recommander des ajustements afin de projeter des contraintes et de générer un mélange et une utilisation de technologie optimale de ressources distribuées. Dans un autre mode de réalisation, lorsque des données historiques sont fournies, des techniques de probabilité peuvent générer une pluralité de solutions possibles. Une fois qu'un utilisateur a confirmé une solution désirée, des comptes-rendus et des graphiques détaillés, des coûts et des solutions financières peuvent être fournis.
PCT/US2002/041390 2001-12-28 2002-12-26 Planification d'analyse financiere previsionnelle et repartition de ressources distribuees WO2003058396A2 (fr)

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CN110852582A (zh) * 2019-10-29 2020-02-28 国网青海省电力公司 一种基于固定投资的配电网规划项目自动优选方法
EP3657407A1 (fr) * 2018-11-23 2020-05-27 Commissariat à l'Energie Atomique et aux Energies Alternatives Procédé d'envoi d'un profil de participation, procédé de pilotage et dispositifs associés
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US11682046B2 (en) 2019-06-19 2023-06-20 FinanceNinja, LLC Systems and methods for implementing a sponsor portal for mediating services to end users
US11875371B1 (en) 2017-04-24 2024-01-16 Skyline Products, Inc. Price optimization system

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Publication number Priority date Publication date Assignee Title
US10984433B1 (en) 2017-04-24 2021-04-20 Skyline Products, Inc. Price optimization system
US11875371B1 (en) 2017-04-24 2024-01-16 Skyline Products, Inc. Price optimization system
EP3657407A1 (fr) * 2018-11-23 2020-05-27 Commissariat à l'Energie Atomique et aux Energies Alternatives Procédé d'envoi d'un profil de participation, procédé de pilotage et dispositifs associés
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US11682046B2 (en) 2019-06-19 2023-06-20 FinanceNinja, LLC Systems and methods for implementing a sponsor portal for mediating services to end users
CN110852582A (zh) * 2019-10-29 2020-02-28 国网青海省电力公司 一种基于固定投资的配电网规划项目自动优选方法

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