# US 2022/0284458 A1 · Risk-constrained Optimization of Virtual Power Plants in Pool and Future Markets > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2022/0284458 A1 - Document ID: US20220284458A1 - Status: Published application - Kind: A1 - Application number: 17/184,889 - Filed: 2021-02-25 - Published: 2022-09-08 - Earliest claimed: 2021-02-25 - Assignees: Mitsubishi Electric Research Laboratories, Inc. - Applicants: Mitsubishi Electric Research Laboratories, Inc. - Inventors: Hongbo Sun - Web page: https://patentgrep.com/patent/US20220284458A1 ## CPC - G06Q 30/0206: {Price or cost determination based on market factors} - G06Q 10/0635: Risk analysis of enterprise or organisation activities - G06Q 50/06: Energy or water supply - 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 ## Abstract Embodiments for distributing energy for an energy system having an energy generation source, an energy storage system and a load. The method including identifying a risk level for the energy system, the risk level having objectives prioritized relative to one another. Calculating an objective function based on values including energy market values, electricity rates, and power producing, storing and consumption. Identifying an Optimized solution for charging or discharging the energy storage system based on the objective function. Controlling the distribution of energy to the energy storage system for charging according to the Optimized solution based on the objective function and discharging according to the Optimized solution. The objective function is Optimized using feasibility constraints, again Optimized using technical constraints and additional constraints. The Optimized solution maximizes an expected total pool market revenue and an expected total future market revenue, while minimizing an expected total energy cost for the energy system. ## Claims ### Claim 1 A method for distributing energy for an energy system having an energy generation source, an energy storage system and a demand load, comprising: identifying a risk tolerance level for the energy system, the risk tolerance level having objectives prioritized relative to one another; calculating an objective function based on values including one or more of energy market values, electricity rates, and power producing, storing and consumption; identifying an Optimized solution for charging or discharging the energy storage system based on the objective function; controlling the distribution of energy to the energy storage system for charging according to the Optimized solution based on the objective function; and controlling the distribution of energy from the energy storage system for discharging according to the Optimized solution, wherein the objective function is Optimized using feasibility constraints generated from a lower bound and an upper bound of an information gap region in the risk tolerance level, and then Optimized using both technical constraints and additional constraints generated from a lower boundary for a profit distribution, wherein the Optimized solution is performed to maximize an expected total pool market revenue and an expected total future market revenue, while minimizing an expected total energy cost for the energy system. ### Claim 2 (depends on 1) The method of claim 1, wherein the energy system is one of, a virtual power plant, virtual energy plant, an energy hub, a multi-generation system or multi-energy systems. ### Claim 3 (depends on 1) The method of claim 1, wherein the objectives prioritized relative to one another include one or more objectives associated with uncertainties of future market prices for future markets, that is prioritized relative to one or more objectives associated with uncertainties of pool market prices, renewable generation and load demand for a pool market. ### Claim 4 (depends on 1) The method of claim 1, wherein the feasibility constraints for low risk tolerable level are information gap constraints associated with uncertainties of future market prices, and generated from a selling price for the lower bound of the information gap region, and a buying price for the upper bound of the information gap region. ### Claim 5 (depends on 1) The method of claim 1, wherein the feasibility constraints for high risk tolerable level are information gap constraints associated with uncertainties of future market prices, and generated from a selling price for the upper bound of the information gap region, and a buying price for the lower bound of the information gap region. ### Claim 6 (depends on 1) The method of claim 1, wherein the technical constraints are second-order stochastic dominance constraints (SDCs) associated with uncertainty of pool market prices, renewable generation and the load demand; wherein the SDCs are given by a step-wise profit probability distribution for stochastic scenarios describing the uncertainty of pool market prices, renewable generation and the load demand. ### Claim 7 (depends on 1) The method of claim 1, wherein the additional constraints are trading related constraints, and scheduling related constraints; wherein trading related constraints include power balance equations between traded power and produced and consumed power for each time period and stochastic scenario, block-wise non-decreasing/non-increasing characteristics for biding/offering curves of future Option contracts and future strip contacts, non-increasing characteristics for offering curves of pool market, relationships between execution of trading block and trading rights for block of future Option contacts for each time period and stochastic scenario, mutual exclusivity for selling and buying Option and strip contracts, maximum allowed selling and buying powers for future Option and strip contracts at each time period, maximum allowed selling powers for pool market at each time period; wherein scheduling related constraints include energy balance equation for storages, minimum and maximum stored energies for storages, maximum charging and discharging capacities for storages, maximum generated powers for wind and solar power plants, maximum load curtailments for loads. ### Claim 8 (depends on 1) The method of claim 1, wherein the energy market values include energy blocks in which trading rights are purchased for future Option contracts, energy blocks purchased to be actually executed for future Option contracts at each time period and stochastic scenario, energy blocks to be traded for future strip contracts, selling powers to pool market at each time period and stochastic scenario; wherein the electricity rates include premium and execution prices for future selling and buying Option contracts, prices for future selling and buying strip contracts, selling prices for pool market at each time period and stochastic scenario; wherein power producing, storing and consumption includes the charging/discharging statuses, powers and related wear cost for storages at each time period and stochastic scenario, the power generated and related wear cost for wind and solar power plants at each time period and stochastic scenario, the load curtailments and related cost for loads at each time period and stochastic scenario. ### Claim 9 (depends on 1) The method of claim 1, wherein the Optimized solution is obtained for a low risk tolerable level (i.e. risk-averse strategy) by solving a problem by maximizing an uncertainty horizon of future market prices under a profit constraint that requires the minimal profit for future prices within price bands defined by the uncertainty horizon and forecasted future prices must be greater than a tolerable profit threshold; wherein the minimal profit is determined by minimizing the expected total profits subject to the constraints of price bands, technical constraints and additional constraints; wherein the tolerable profit threshold is determined by multiplying a pre-determined less than one factor to the profit obtained by maximizing the expected total profits subject to the constraints of price bands, technical constraints, and additional constraints using the forecasted future prices. ### Claim 10 (depends on 1) The method of claim 1, wherein the Optimized solution is obtained for a low risk tolerable level (i.e. risk-averse strategy) by solving a problem by maximizing an uncertainty horizon of future market prices subject to a set of constraints by setting the selling prices using lower bounds of selling price bands defined by the uncertainty horizon and forecasted future selling prices, the buying prices using upper bounds of buying price bands defined by the uncertainty horizon and forecasted future buying prices; wherein the set of constraints include a profit constraint that requires the profit must be greater than a tolerable profit threshold, along with technical constraints and additional constraints; wherein the tolerable profit threshold is determined by multiplying a pre-determined less than one factor to the profit obtained by maximizing the expected total profits subject to the constraints of price bands, technical constraints, and additional constraints using the forecasted future prices. ### Claim 11 (depends on 1) The method of claim 1, wherein the Optimized solution is obtained for a high risk tolerable level (i.e. risk-seeking strategy) by solving a problem by minimizing an uncertainty horizon of future market prices under a profit constraint that requires the maximal profit for future prices within price bands defined by the uncertainty horizon and forecasted future prices must be greater than a tolerable profit threshold; wherein the maximal profit is determined by maximizing the expected total profits subject to the constraints of future price bands, technical constraints and additional constraints; wherein the tolerable profit threshold is determined by multiplying a pre-determined greater than one factor to the profit obtained by maximizing the expected total profits subject to the constraints of price bands, technical constraints, and additional constraints using the forecasted future prices. ### Claim 12 (depends on 1) The method of claim 1, wherein the Optimized solution is obtained for a high risk tolerable level (i.e. risk-seeking strategy) by solving a problem by minimizing an uncertainty horizon of future market prices subject to a set of constraints by setting the selling prices using upper bounds of selling price bands defined by the uncertainty horizon and forecasted future selling prices, the buying prices using lower bounds of buying price bands defined by the uncertainty horizon and forecasted future buying prices; wherein the set of constraints include a profit constraint that requires the profit must be greater than a tolerable profit threshold, along with technical constraints and additional constraints; wherein the tolerable profit threshold is determined by multiplying a pre-determined greater than one factor to the profit obtained by maximizing the expected total profits subject to the constraints of price bands, technical constraints, and additional constraints using the forecasted future prices. ### Claim 13 A system for distributing energy for an energy system including a renewable generating source, an energy storage system and a conversion system, wherein the energy storage system stores energy from the renewable generating source and a grid, and supplies energy to the demand load and the grid, and wherein the conversion system directs a flow of energy between the energy storage system, the renewable generating source and the grid, the system comprising: a centralized control (CC) system is configured to identify a risk tolerance level for the energy system, the risk tolerance level having objectives prioritized relative to one another; calculate an objective function based on values including one or more of energy market values, electricity rates, power production and consumption; and identify an Optimized solution for determining (a) when energy from the renewable generating source and the grid are to be authorized to flow to the energy storage system or the demand load, according to the Optimized solution based on the objective function, and (b) when energy from the energy storage system are to be authorized to flow to the demand load and the grid, according to the Optimized solution, wherein the objective function is Optimized using feasibility constraints generated from a lower bound and an upper bound of an information gap region in the risk tolerance level, and another Optimization using both technical constraints and additional constraints generated from a lower boundary for a profit distribution, and wherein the Optimized solution is performed to maximize an expected total pool market revenue and an expected total future market revenue, while minimizing an expected total energy cost for the energy system. ### Claim 14 (depends on 13) The system of claim 13, wherein the CC system is in communication with the renewable generating source, the energy storage system, the conversion system, the grid, data storage, a transceiver and communication network, such that the CC system includes a network cloud. ### Claim 15 (depends on 13) The system of claim 13, wherein the risk tolerance level for the energy system is communicated to the CC system via a network cloud by the energy system. ### Claim 16 (depends on 13) The system of claim 13, wherein the energy storage device is a mobile energy storage device that stores energy to other energy storage devices. ### Claim 17 A system for distributing energy for a virtual power plant (VPP), the VPP including a renewable generating source and an energy storage device, and a conversion system, wherein the energy storage device is to store energy from the renewable generating source and a grid, and supply energy to a local demand load and the grid, and wherein the conversion system is to direct a flow of energy between the energy storage device, the renewable generating source and the grid, the system comprising: a centralized control (CC) system in communication with the renewable generating source, the energy storage device, the grid and the conversion system, and is configured to identify a risk tolerance level for the energy system, the risk tolerance level having objectives prioritized relative to one another; calculate an objective function based on values including one or more of energy market values, electricity rates, power production and consumption; and identify an Optimized solution formulated as a risk-constrained Optimization problem for determining (a) when energy is to be authorized to flow from the renewable generating source and the grid to the energy storage device or the demand load, according to the Optimized solution based on the objective function, and (b) when energy is to be authorized to flow from the energy storage device to the demand load and the grid, according to the Optimized solution, wherein the objective function is Optimized using feasibility constraints generated from a lower bound and an upper bound of an information gap region in the risk tolerance level, and then another Optimization using both technical constraints and additional constraints generated from a lower boundary for a profit distribution, and wherein the Optimized solution is performed to maximize an expected total pool market revenue and an expected total future market revenue, while minimizing an expected total energy cost for the energy system. ### Claim 18 (depends on 17) The system of claim 17, further comprising based on the objective function, generating, at the VPP controller server, a VPP demand response (DR) event schedule that includes a charge/discharge schedule of one or more energy source devices and a consumption/curtailment schedule for one or more energy load devices; and communicating in real time, by the VPP controller server, the VPP DR event schedule to one or more VPP client servers, the VPP DR event schedule including control signals that are configured to affect an operating condition of one or more of the devices that are controlled by the VPP client servers. ### Claim 19 (depends on 17) The system of claim 17, further comprising based on whether the reserve capacity request is either greater than or less than the updated reserve capacity forecast; and control a power output and power consumption of the subset of DERs with output of the dispatches in order to meet the reserve capacity request. ### Claim 20 (depends on 17) The system of claim 17, further comprising controlling of the at least one electrical power asset, such that electric power is provided, electric power is consumed, or electric power is provided and electric power is consumed.