← Back to Patent Explorer
US6021402A Expired / Lapsed US ⏱️ 0.0 years remaining

Risk management system for electric utilities

Assignee: International Business Machines Corp
•
Filing: 1997-06-05
•
Granted: 2000-02-01
•
Expires: 2017-06-05
📄 Original PDF 🌐 Google Patents 🔄 Sync Now

Abstract

Abstract A computer implemented risk-management system schedules the generating units of an electric utility while taking into consideration power trading with other utilities and the stochastic load on the utility system. The system provides the user with a tool that generates multiple load forecasts and allows the user to vary the fuel price between the different scenarios and the different periods of the planning horizon. The tool allows the user to model accurately the uncertain trading transactions and the changing fuel prices to meet the electric demand of customers at a minimal cost while making the maximum profit possible from power trading. The tool also allows the user to apply any set of linear constraints to fuels. A mathematical model of the problem is solved to provide the status of each generator at each time period of the planning horizon under each given scenario, the load on each generator during each period in which it is operating, an optimal fuel mix for each generating unit, and the prices for purchasing and selling power in the periods of the planning horizon.

Inventors

  • 👤 Samer Takriti

Bibliographic Details

Application Number —
Jurisdiction US
Data Source google_patents
Last Synchronized 2026-10-27 08:37
CPC Classifications
G; G06; G06Q; G06Q50/00; G06Q50/06; G06Q10/00; G06Q10/06; Y; Y04; Y04S; Y04S10/00; Y04S10/50

Claims Analysis

Total Claims: 9
Claim 1
Independent Claim 290 words
In a risk management system that schedules generating units of an electric utility while taking into consideration power trading with other utilities and a stochastic load on the utility system, a computer implemented method comprising the steps of: inputting load forecast, trading forecast and generator and fuel properties; generating load scenarios as a scenario tree that approximates future uncertainties in load and trades; computing an initial approximate value for the cost per British Thermal Unit (BTU); obtaining a load on each generator under any scenario and, given the load on each generator, determining an optimal fuel allocation to obtain a cost function of generating a Megawatt Hour (MWH) of power on each generator; solving a stochastic unit-commitment model to obtain a generation of each unit at each time period under different scenarios which can be used to compute heat-input needed for each unit throughout a planning horizon; solving a linear program in which decision variables are amount of fuel burnt on each unit at each time period, wherein decision variables have a component for each possible fuel in the model and the linear program is solved subject to a constraint that fuel fed to a generator must be sufficient to generate the heat needed; determining if a current fuel allocation is the same as before and, if not, computing a new cost function for each generator for all fuels fed to a given generator, the new cost function being used in resolving unit commitment; repeating the steps of solving the stochastic unit-commitment model until the fuel mixtures and the load on the generators approach an optimal solution; and outputting fuel consumption, generation requirement and load for each generator of the utility at each period and the fuels to be used.
Claim 2
Dependent Claim 290 words
In a risk management system that schedules generating units of an electric utility while taking into consideration power trading with other utilities and a stochastic load on the utility system, a computer implemented method comprising the steps of: inputting load forecast, trading forecast and generator and fuel properties; generating load scenarios as a scenario tree that approximates future uncertainties in load and trades; computing an initial approximate value for the cost per British Thermal Unit (BTU); obtaining a load on each generator under any scenario and, given the load on each generator, determining an optimal fuel allocation to obtain a cost function of generating a Megawatt Hour (MWH) of power on each generator; solving a stochastic unit-commitment model to obtain a generation of each unit at each time period under different scenarios which can be used to compute heat-input needed for each unit throughout a planning horizon; solving a linear program in which decision variables are amount of fuel burnt on each unit at each time period, wherein decision variables have a component for each possible fuel in the model and the linear program is solved subject to a constraint that fuel fed to a generator must be sufficient to generate the heat needed; determining if a current fuel allocation is the same as before and, if not, computing a new cost function for each generator for all fuels fed to a given generator, the new cost function being used in resolving unit commitment; repeating the steps of solving the stochastic unit-commitment model until the fuel mixtures and the load on the generators approach an optimal solution; and outputting fuel consumption, generation requirement and load for each generator of the utility at each period and the fuels to be used.
Claim 3
Dependent Claim 43 words
The computer implemented method of claim 1 wherein a generator may use more than one fuel at any time period, the step of computing an initial approximate value for the cost per BTU being performed by using the fuel with the least cost.
Claim 4
Dependent Claim 90 words
The computer implemented method of claim 1 wherein the step of obtaining a load on each generator is performed by the steps of: constructing cost functions using an average fuel cost; for each scenario and period in a planning horizon, ranking the generators in an ascending order of average marginal cost at a maximum production level, selecting enough generators so that the load and spinning-reserve constraints are met, and solving an economic power dispatch; determining a feasible solution for a stochastic unit commitment model; and solving an economic power dispatch.
Claim 5
Dependent Claim 106 words
The computer implemented method of claim 1 wherein a generator may use more than one fuel at any time period, the step of computing an initial approximate value for the cost per BTU comprises the steps of: approximating the cost per BTU by using a fuel with the least cost; solving a unit commitment problem to determine the amount of power generated by each generating unit; finding an optimal fuel mixture for each generator at each time period and under each scenario using linear programming; and repeating the previous two steps as long as the fuel mixture obtained from the linear programming solution changes between iterations.
Claim 6
Dependent Claim 48 words
The computer implemented method of claim 1 wherein a user of the risk management system may provide a scenario tree which is processed to define bundle constraints, two scenarios being members of the same bundle at time t if their loads are the same up until time t.
Claim 7
Dependent Claim 249 words
The computer implemented method of claim 5 wherein the processing of the scenario tree to define bundle constraints comprises the steps of: setting a predecessor of Scenario 1 to 1, a split time of Scenario 1 to 1, and a scenario index, s, to 2; comparing a load of Scenario s to a load of Scenario 1 to determine a split time of s as a first point in time at which the load of Scenario s is different from that of Scenario 1 and, at this point, setting a predecessor of s to 1; introducing a new variable, s', and initializing s' to 2; comparing the load of s to all other scenarios s'=1, . . . , s-1, in order to determine a split time and predecessor of s; determining whether s' is less than s and, if so, incrementing s by 1, but otherwise, comparing split times of s and s'; if split times of s and s' are not equal, indicating that s' should not be used as a predecessor to s, incrementing s' by 1, but otherwise, comparing the loads of s and s' to obtain a new split time for s and setting the predecessor of Scenario s to s' before incrementing s' by 1; when all the scenarios in the scenario tree are scanned, checking s against a total number of scenarios, S, and if s=S, terminating the execution but otherwise, the execution is continued until all scenarios, S, have been processed.
Claim 8
Dependent Claim 188 words
In a risk management system that schedules generating units of an electric utility while taking into consideration power trading with other utilities and a load on the utility system, a computer implemented method comprising the steps of: inputting factor data comprising load forecast, trading forecast, spot market estimates, and generator properties and fuel properties, costs and constraints; generating load scenarios that approximate future uncertainties in at least some of the factor data; obtaining a load on each generator under at least one of said scenarios and determining an optimal fuel allocation to obtain a cost function of generating power on each generator; solving a plurality of commitment models to obtain a generation of each unit at each time period under different scenarios which can be used to compute energy input needed for each generator unit throughout a planning horizon; selecting an optimal fuel mixture and a load on the generators based on the models; and outputting fuel consumption, generation requirement and load for each generator of the utility at each period and the fuels to be used and outputting average production costs at each period under each scenario.
Claim 9
Dependent Claim 188 words
In a risk management system that schedules generating units of an electric utility while taking into consideration power trading with other utilities and a load on the utility system, a computer implemented method comprising the steps of: inputting factor data comprising load forecast, trading forecast, spot market estimates, and generator properties and fuel properties, costs and constraints; generating load scenarios that approximate future uncertainties in at least some of the factor data; obtaining a load on each generator under at least one of said scenarios and determining an optimal fuel allocation to obtain a cost function of generating power on each generator; solving a plurality of commitment models to obtain a generation of each unit at each time period under different scenarios which can be used to compute energy input needed for each generator unit throughout a planning horizon; selecting an optimal fuel mixture and a load on the generators based on the models; and outputting fuel consumption, generation requirement and load for each generator of the utility at each period and the fuels to be used and outputting average production costs at each period under each scenario.

References Cited (Backward Citations)

US3124699A Cited
US2841331A Cited
US2923832A Cited
US3117221A Cited
US3229110A Cited
US3932735A Cited
US4604714A Cited
US5553003A Cited
US5432710A Cited
US5621654A Cited
US5734586A Cited

USPTO Annuity & Maintenance Windows

Annuity Stage Window Opens Payment Deadline Surcharge Period Until Estimated Fee Status
3.5 Years (Year 4) 2003-01-31 2003-08-02 2004-02-01 $2000.00 Window Closed / Lapsed
7.5 Years (Year 8) 2007-01-31 2007-08-02 2008-02-01 $3760.00 Window Closed / Lapsed
11.5 Years (Year 12) 2011-01-31 2011-08-02 2012-02-01 $7700.00 Window Closed / Lapsed

Assigned Categories & Tags

No categories assigned to this patent yet.

Assign Category Tag