WO2024255933A1 - Équilibrage de réseaux de distribution d'énergie électrique à l'aide d'une optimisation multiniveau - Google Patents

Équilibrage de réseaux de distribution d'énergie électrique à l'aide d'une optimisation multiniveau Download PDF

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WO2024255933A1
WO2024255933A1 PCT/CZ2023/000027 CZ2023000027W WO2024255933A1 WO 2024255933 A1 WO2024255933 A1 WO 2024255933A1 CZ 2023000027 W CZ2023000027 W CZ 2023000027W WO 2024255933 A1 WO2024255933 A1 WO 2024255933A1
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operating parameters
level
market
participant
optimization
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Jakub MAREČEK
Vyacheslav KUNGURTSEV
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Czech Technical University In Prague
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/008—Circuit arrangements for power supply or distribution technologies responsive to energy trading
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2103/00—Details of circuit arrangements for mains or AC distribution networks
    • H02J2103/30—Simulating, planning, modelling, reliability check or computer assisted design [CAD] of electric power networks

Definitions

  • the invention relates to a method of balancing electric power systems using multilevel optimization.
  • the method employs steps which shall primarily result in channeling power flows by clearing electricity markets, i.e., matching bids, close to real time.
  • the invention relates to a device adapted to execute this method.
  • the present invention takes a more rational approach to balancing the power grid and focuses primarily on directing power flows where they are actually required, i.e., on demand.
  • the advantage of this invention is that it can be implemented immediately without incurring financial costs significantly higher than those of shortening the trading interval.
  • the use of the method according to the present invention optimizes the use of resources and, above all, leads to the stabilization of the energy network, since it does not create conditions for overproduction and can direct energy flows where they are actually required.
  • the transmission constraints [29] can be formulated as multi- variate polynomial constraints [17], for which in isolation there are globally convergent methods, but for the combination of polynomial constraints in one level and the combinatorial constraints in the other level of a bi-level problem, only heuristic methods [18, 7, 8, 6, 42, 37, 26] are available. In particular, it was not known whether the solutions are globally optimal for the bi-level problem with polynomial constraints and integer decision variables.
  • This invention provides the first methods for the problem, considering both of the challenges above, for three different ways of modelling power-systems dynamics and correspondingly the transmission constraints. Additionally, formal guarantees of global convergence could be derived. [0013]
  • the closest piece of related work in the academic literature is [40], which does not consider the robustness of the market clearing, considers only a basic model of swing dynamics at the TSO level, assumes convexity at the market-participant level, which is not realistic. Furthermore, it guarantees only local convergence to an arbitrarily bad solution.
  • US6775597B1 relates to a Security-Constrained Optimal Power Flow (SCOFF) process employing a quadratic programming (QP) primal-dual interior point (IP) solution method.
  • QP quadratic programming
  • IP primal-dual interior point
  • the IP method efficiently solves practical SCOPF problems involving large numbers of contingencies and controls in preventive and preventive/corrective operating modes.
  • An EMS system is described incorporating the inventive SCOPF process. This is inferior to the proposed invention, as it does not consider the combinatorial nature of bids and the need to clear markets using those.
  • Another document US9953117B2 relates to a method for solving a two-stage non- linear stochastic formulation for the economic dispatch problem under renewable- generation uncertainty. Certain generation decisions are made only in the first stage and fixed for the subsequent (second) stage, where the actual renewable generation is realized. The uncertainty in renewable output is captured by a finite number of scenarios. Any resulting supply-demand mis-match must then be alleviated using high marginal-cost power sources that can be tapped in short time frames.
  • the solution implements two outer approximation algorithms to solve this nonconvex optimization problem to optimality including the application of a decomposition approach derived from the Alternating Direction Method of Multipliers (ADMM) algorithm. This is inferior to the proposed invention, as it does not consider the combinatorial nature of bids and the need to clear markets using those.
  • ADMM Alternating Direction Method of Multipliers
  • the document US9912153B2 relates to a method for controlling the ratio between injected and extracted electric energy in an electric energy supply grid with a number of grid participants, which are selected from a group including producers, consumers, and storage devices, with at least two of the group being included.
  • a grid state variable is used as a control variable, the value of said variable depending on the ratio between inserted and extracted electric energy and being ascertainable from the grid by the grid participants.
  • the invention is characterized by a number of grid participants ascertain the grid state variable from the grid and use said variable at least indirectly to control the grid in a decentralized manner based on a respective specific grid participant behavior. This s inferior to the proposed invention, as it does not consider non-convexities of the problem, such as the combinatorial nature of bids and non-convex nature of AC transmission constraints.
  • One embodiment includes a node controller including a distributed power control application; a plurality of node operating parameters describing the operating parameter of a node in an unbalanced network; wherein the processor is configured by the distributed power control application to: send node operating parameters to nodes in the set of at least one node; receive operating parameters from the nodes in the set of at least one node; calculate a plurality of updated node operating parameters using an iterative process to determine updated node operating parameters using the node operating parameters that describe the operating parameters of the node, and the operating parameters of the set of at least one node, where each iteration in the iterative process involves evaluation of a subproblem; and adjust node operating parameters.
  • This is inferior to the proposed invention, as it is restricted to radial feeders, and does not consider market clearing.
  • Present invention relates to a method of balancing electric power systems using multilevel optimization, wherein the method employs three main components in each of three embodiments to address the challenges represented by the complex problem of balancing the grid by clearing markets near real time.
  • the method comprises following steps: a. using a robust bi-level formulation [14], which extends previous work on bi-level polynomial optimization [21]; this makes it possible to consider both sides of the market as a bi-level optimization problem [16], while additionally considering the worst-case realization of certain random variables (e.g., production limits and demand) within some uncertainty sets around their nominal values. This makes it possible to produce solutions robust against all realizations of uncertainty within the uncertainty sets. Technically, this adds one or two more levels to the multi-level optimization problem. b. using a semidefinite-programming relaxation of the polynomial optimization problem [17] to capture transmission constraints at the transmission-system operator (market operator) level.
  • modelling the transmission constraints makes it possible to obtain market clearing that is feasible with respect to a given model of the power systems dynamics. This improves over the current trial-and-error approach, wherein the market is cleared, the feasibility is simulated by a simulator of power-systems dynamics, and if not feasible, the market clearing needs to be adjusted.
  • the use of the semidefinite programming allows for a very efficient consideration of the transmission constraints in the alternating-current model. c. using a convexification of the market-participant level to allow for efficient execution. In general, this reformulates non-convex problem at the market-participant level to allow for efficient optimization methods.
  • the option for the convexification considered in this embodiment is the use of the so-called extended formulation [32, 25].
  • the so-called extended formulation is an equivalent, convex higher- dimensional formulation of the non-convex problem capturing the operational constraints of the market participant (e.g., active power output within a range, ramping constraints in the so-called unit commitment problem). While the higher-dimensional formulation may have an exponentially higher number of variables or inequalities, compared to the traditional "compact" formulations (e.g., of unit commitment), these have recently been shown [25, Table 5] to be competitive with the best compact formulations in terms of run- time.
  • step c In an advantageous embodiment of present invention, one replaces the convexification of step c of [21 ] with the so-called semidefinite programming (SDP) relaxation [3].
  • SDP semidefinite programming
  • the method steps a and b of the embodiment 1 are employed and the method step c is replaced with the following wording of step c so that the method according to embodiment 2 comprises steps: a. using a robust bi-level formulation [14], which extends previous work on bi-level polynomial optimization [21].
  • the advantages are the same as in previous embodiment.
  • the advantages are the same as in previous embodiment.
  • SDP semidefinite programming
  • the method according to this embodiment advantageously comprises any of following steps, and preferably all those steps, wherein: a. Aside of evaluating a present state of uncertainty as in Embodiments 1 and 2, the method employs defining the projected uncertainty moving forward in time, preferably followed by cross verifying the uncertainty between the nonconvex polynomial formulation using ellipsoidal tube methods as used in robust model predictive control [19] and SDP techniques [13] tailored specifically to polynomial systems [41]. b.
  • the method employs using a trajectory of states of the dynamics, by incorporating time and the predicted operation in the future by solving optimal control problems using time-dependent SDRs.
  • Time-dependent SDRs or time-varying polynomial optimization problems and SDRs were introduced formally in [2], and novel algorithms that carefully use solution estimates at a current time to quickly obtain ones at the next time interval are in extensive development.
  • the method employs step of combining the continuous time scale and discrete time scales and analyzing stability of the related systems [33]. This is advantageous in that the solvers for time-varying semidefinite program may provide guarantees as to the ability to track the trajectory of optimal solutions.
  • the invention relates to a device for balancing electric power systems using multi-level optimization, comprising a network interface, a memory containing a plurality of operating parameters describing the dynamics of an electric power system; and a plurality of participant operating parameters describing operating parameters for a set of at least one participant selected from the group consisting of producers and consumers and storage providers; a processor, configured to: receive operating parameters from the participants; upon receipt of operating parameters, calculating a plurality of updated operating parameters using an iterative process to determine the updated operating parameters as values of an optimizer using other operating parameters as coefficients in a multi-level optimization problems; and immediately upon calculating, sending participant operating parameters based on the calculated plurality of updated operating parameters.
  • the participants comprise of operators of generators (genco).
  • Each generator may have its operational constraints described, such as outages planned and unplanned, the lower and upper limits on active and reactive power outputs, which may be time-varying, and limits on so-called ramping, i.e., the rate at which the active and reactive power outputs may change.
  • the operating parameters received from the participants comprise of distributional forecasts of production and bids, which have not been matched so far.
  • Each distributional forecast of production may be a sequence of bi-variate distributions on the support given by the lower and upper limits on active and reactive power outputs, indexed by time steps in the future, which suggests the probability of producing a particular active and reactive power at a given time.
  • Each distributional forecast of bids may be a sequence of bi-variate distributions on the support given by the lower and upper limits on active and reactive power demand, indexed by time steps in the future, which suggests the probability of demanding a particular active and reactive power at a given time.
  • the updated operating parameters comprise of information on the bids, which have been cleared.
  • these include the active and reactive power outputs.
  • For demand-side bids these include the active and reactive power demand. These quantities may be suggested by their nominal values, while knowing that there is uncertainty as suggested by the distributional forecasts of [25].
  • the updated operating parameters are obtained using multi-level optimization with robustness properties.
  • the robust multi-level optimization may extend the robust bi-level formulation of [14].
  • Figure 1 presents a high-level overview of the multi-level optimization view of the problem of clearing a market for electricity.
  • the nominated electricity market operator clears the market, while clearing as many bids as possible, minimizing the risks involved, or both.
  • Figure 2 presents two examples of the constraints that model power systems dynamics, which underlies the stability of the power system in a transmission system, as imposed by the transmission system operator. This refers to advantageous step of defining the projected uncertainty moving forward in time as described in Embodiment 3.
  • FIG. 201 it presents an overview of a rather detailed model power systems dynamics model, which considers: (a) inertia of the rotating machinery, where the kinetic energy of the rotating mass is proportional to its moment of inertia and the square of its angular velocity, or more sophisticated ones; (b) the operations of local feedback controller of the rotating machinery, known as governors or droop controllers. These act on the steam valve of the turbine, for instance.
  • S is the set of generator buses
  • ⁇ delta J is the rotor angle at generator i ⁇ in S_G
  • ⁇ omegaj is the rotor speed at generator i ⁇ in S_G
  • TJMi ⁇ is the mechanical power output of generator i ⁇ in S_G
  • MJ is the inertia constant of generator i tin S_G
  • DJ is the damping torque coefficient of generator i ⁇ in S_G.
  • E_(fdi) is the excitation output voltage
  • VJ is the bus i voltage magnitude
  • V_ ⁇ Ri ⁇ is the voltage regulator output
  • V_ ⁇ refi ⁇ is the reference voltage
  • R_(Fi ⁇ is the exciter rate feedback
  • S_ ⁇ E ⁇ (E_[fdi ⁇ ) is the field saturation function A_[ei ⁇ A ⁇ B_ ⁇ ei ⁇ E_ ⁇ fdi ⁇
  • K_ ⁇ Ei ⁇ is the exciter gain
  • K_ ⁇ Ai ⁇ is the voltage regulator gain
  • K_ ⁇ Fi ⁇ is the rate feedback gain
  • TJEi ⁇ is the exciter time constant
  • T_ ⁇ Ai ⁇ is the voltage regulator time constant
  • T_[Fi ⁇ is the rate feedback time constant
  • ⁇ thetaj is the bus voltage phase angle
  • R_ ⁇ st ⁇ is the armature resistance
  • E_ ⁇ di ⁇ * is the d-axis component of the internal voltage
  • E_ ⁇ qi ⁇ ‘ is the q-axis component of the
  • Figure 3 presents a brief overview of multi-level optimization.
  • 301 the problem on N levels is stated succinctly.
  • 302 the problem description is expanded.
  • FIG. 5 presents an overview of the data received by the balancing mechanism.
  • TS0 1 wishes to set apparent power limits and voltage magnitude bounds.
  • GenCos 1 and 2 and DisCo 1 and 2 submit their bids.
  • Example 2 we consider Embodiment 2 on the Example of Figure 5.
  • the inclusion of a solution within the feasible set of a semidefinite-programming relaxation of the polynomial optimization problem corresponding to steady states of optimal power flows (Figure 2, 202-203. corresponding to 503 of Figure 5) could be modelled by a non-smooth, but convex indicator functions g (301 in Figure 3).
  • the semidefinite-programming relaxation [3] could be modelled by a non-smooth, but convex indicator functions g (301 in Figure 3).

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Abstract

La présente invention concerne un procédé d'équilibrage de réseaux de distribution d'énergie électrique à l'aide d'une optimisation multiniveau, le procédé utilisant des étapes qui devraient principalement conduire à la canalisation de flux d'énergie par équilibrage de marchés de l'électricité, c'est-à-dire appariement des offres, en temps quasi réel. Le procédé comprend l'étape consistant à utiliser une formulation à deux niveaux robuste, tout en utilisant une relaxation de programmation semi-définie du problème d'optimisation polynomiale pour capturer des contraintes de transport au niveau de l'opérateur de système de transport (opérateur de marché) et en utilisant une formulation étendue du niveau participants au marché pour optimiser le temps d'exécution de l'opération afin de rendre efficacement convexe le niveau participants au marché.
PCT/CZ2023/000027 2023-06-16 2023-06-16 Équilibrage de réseaux de distribution d'énergie électrique à l'aide d'une optimisation multiniveau Ceased WO2024255933A1 (fr)

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CN119582349A (zh) * 2025-02-08 2025-03-07 华南理工大学 一种基于波动分析识别关键约束的鲁棒机组组合优化方法
CN121076791A (zh) * 2025-11-06 2025-12-05 国网四川省电力公司达州供电公司 基于主从策略的微电网群协同调度方法
CN121187133A (zh) * 2025-10-14 2025-12-23 江苏卓易信息科技股份有限公司 一种基于主从架构的迭代学习控制方法

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