Independent System Operator Market Research Articles

Forecast Aggregated Supply Curves in Power Markets Based On LSTM Model

One of the key steps for optimal bidding in power markets is to estimate the rivals’ bidding behaviors. However, for most participants, it would be difficult to directly forecast the rivals’ individual bids due to the information privacy and volatile characteristics of individual bidding behaviors. From another point of view, the aggregation of individual bids, denoted as aggregated supply curve (ASC), might be helpful to offset the uncertainties of individual bidding behaviors and can be used as reference for optimal bidding. In fact, the real ASC data contains bidding information from thousands of participants, which would be formulated with high dimensionality and unstructured formats, not applicable for general forecasting methods. Thus, a novel data-driven ASC forecasting framework based on long-short term memory (LSTM) model and corresponding data processing techniques is proposed in this paper. In detail, A paradigmatic data integration method is proposed to fix the unstructured data formats. A feature extraction method is developed to simplify the high dimensionality of ASC. Then, a LSTM model is customized to forecast ASCs. At last, real data from Midcontinent Independent System Operator market in the U.S. are utilized to demonstrate the forecasting performance of the proposed framework.

Distribution Market-Clearing and Pricing Considering Coordination of DSOs and ISO: An EPEC Approach

Distribution-level electricity market provides a platform for trading energy and grid services from large-amount of small-scale distributed energy resources (DERs) located on distribution grids. The behavior of the DERs in the distribution electricity market may ultimately impact the market-clearing and locational marginal prices (LMPs) in the wholesale market. This paper proposes a bi-level optimization model for distribution market clearing and distribution locational marginal pricing (DLMP) considering the interactions between distribution and transmission wholesale markets. In the proposed model, the upper-level model represents the distribution system operator (DSO) market-clearing and the lower-level model represents the wholesale market-clearing by the independent system operator (ISO). The LMP at the substation will impact the DER dispatch and power demands of the DSO as well as the DLMP. In turn, the power demands of the DSO will further impact the ISO market and its LMPs. The equilibrium problem with equilibrium constraints (EPEC) approach is applied to find the equilibria of multiple DSOs and the ISO. The EPEC problem is transformed into a single-level mixed-integer convex problem in order to allow for efficient solving. The effectiveness of the proposed model and solution method are demonstrated through case studies.

Integrating High DER-Penetrated Distribution Systems Into ISO Energy Market Clearing: A Feasible Region Projection Approach

Primary distribution systems are usually simplified as fixed or flexible loads in the Independent System Operator (ISO) energy market clearing with favorable computational features. However, in emerging distribution systems with an increasing penetration of distributed energy resources (DER), this simplify-cation could easily fail to capture economic features of DERs and internal limits of distribution systems, triggering line congestion and under/over-voltage issues. To this end, a feasible region projection-based approach is proposed in this paper to optimally integrate high DER-penetrated distribution systems into the ISO energy market clearing, while effectively capturing configuration details of distribution systems and fully respecting their internal physical limits such as voltage and line flow constraints. Numerical studies show efficacy of the proposed approach in achieving the optimal integration of high DER-penetrated distribution systems into the ISO energy market, while: (i) not requiring ISO directly formulating full distribution systems with exhaustive variables and constraints; and (ii) not necessitating an iterative procedure to interact ISO with distribution systems, thus compatible to the current ISO market practice.

Plug-in Electric Vehicle Behavior Modeling in Energy Market: A Novel Deep Learning-Based Approach With Clustering Technique

Growing penetration of Plug-in Electric Vehicles (PEVs) in the transportation fleet and their subsequent charging demands introduce substantial intermittency to the electric load profile which imposes techno-economic challenges on power distribution networks. To address the uncertainty in demand, a novel deep learning-based approach equipped with a hybrid classification task is developed which can take into account the travel characteristics of the PEV owners. The classification structure helps us scrutinize the PEVs demand by allocating a specific forecasting network to each cluster of travel behavior patterns. In our hybrid classification task, first, an unsupervised classifier discerns hidden travel-behavior patterns between the historical PEVs data by clustering them; then, a supervised classifier directs each new PEV data to its appropriate cluster-specific forecasting network. The deep learning-based forecasting and classification networks are constructed based on the Long Short-Term Memory networks to investigate long- and short term features in PEV behaviors. The data-driven structure of our proposed method enables us to observe and preserve the correlation between PEV travel data parameters (departure time, arrival time and traveled distance) and avoid the generation of unrealistic travel samples found in scenario-based approaches. To verify the effectiveness of the proposed method in practical environments, we have studied the impact of the precise forecasting of the PEVs demand in an aggregator’s financial profit in the energy market of the California Independent System Operator market. The numerical results confirm the outstanding performance of our proposed deep learning-based method in forecasting PEVs demand against benchmark approaches in this field such as Monte Carlo, Quasi-Monte Carlo, and Copula with only a 6.77% error in comparison with real data.

Scenario-Based Optimal Bidding Strategies of GENCOs in the Incomplete Information Electricity Market Using a New Improved Prey—Predator Optimization Algorithm

In order to find out the optimal bidding strategies (BSs) of generating companies (GENCOs) in a competitive electricity market, it is necessary to solve a bilevel optimization problem. The first level of the problem relates to the GENCOs to strategically bid, and the second level solves the independent system operator's market clearing problem based on the maximization of social welfare. In order to model the incomplete information of participants in the market about cost coefficients of opponents and their forecast errors, a scenario-based programming framework is presented. In addition, a roulette wheel mechanism is used for scenario-generation process so that the forecast errors of coefficients are considered as random variables with known probability distribution functions. Then, each GENCO solves the bilevel optimization problem and maximizes its expected profit function. These bilevel problems are nonconvex, and the mathematical-based optimization technique is unable to handle the problem and obtain the nearly global optima. In order to resolve this issue, a novel prey–predator optimization algorithm is suggested to solve the first level of the bilevel problem and using the iterative method to find out the supply function equilibrium that is the optimal BSs of GENCOs. Applying to the IEEE 57- and 118-bus test systems with incomplete information studies, the performance of the proposed approach is successfully approved.

An environmental/techno-economic approach for bidding strategy in security-constrained electricity markets by a bi-level harmony search algorithm

In this paper, a new approach is presented for developing optimal double-sided bidding strategy in security-constrained electricity markets by considering emission of pollutants, as further objectives. In the proposed methodology, both Generation Companies (GenCos) and Distribution Companies (DisCos) try to maximize their profit by implementation of optimal strategies, whiles they have incomplete information about their rivals and market mechanism of payment is locational marginal pricing. In addition, each participant provides its strategic bids based on supply function equilibrium model and it modifies its bidding strategies until Nash equilibrium points are computed. The proposed approach is modeled as a bi-level optimization problem with the upper sub problem addressing individual GenCos and DisCos and the lower sub problem addressing the Independent System Operator (ISO). The upper level maximizes the individual market participant's profit and the lower one solves the ISO's market clearing problem for maximizing Community Welfare Function (CWF). A Self-adaptive Global-based Harmony Search Algorithm (SGHSA) is used to obtain optimal bidding strategies. The proposed methodology has been implemented on the 6-machine 8-bus system test system to demonstrate the feasibility and effectiveness of the proposed approach. Simulation results illustrate the profitableness of the newly developed approach in the obtaining optimal bidding strategies.

A hybrid model for integrated day‐ahead electricity price and load forecasting in smart grid

Load and price forecasting are two key issues for market participants and system operators in electricity markets. Most existing works predict load and price separately. However, a dynamic pricing type scenario is envisioned in smart grid, where consumers may have the ability to react to constantly changing price signals by shifting electricity usages from expensive hours to others, which would consequently impact electricity prices. Thus, price and load signals are strongly coupled, and previous separated forecast models may not be as effective. In this study, a two-stage integrated price and load forecasting framework is developed. The first stage provides initial price and load forecasts separately, and the second stage considers load and price interaction with initial forecasts as inputs. At each stage, a hybrid time-series and adaptive wavelet neural network (AWNN) model is used, in which multivariate autoregressive integrated moving average catches the linear relationship of price and load log return series, generalised autoregressive conditional heteroscedastic unveils heteroscedastic character of residuals and AWNN presents non-linear impacts. Several criteria such as average mean absolute percentage error and error variance are used to measure the forecasting accuracy. Illustrative forecasting examples of the New York Independent System Operator market are presented to show the efficiency of the proposed method.

Short-Term Electricity Market Simulation for Pool-Based Multi-Period Auctions

This paper presents a simulation model based on the diagonalization algorithm for the study of the short-term operation of a pool-based oligopolistic electricity market with multi-period auctions. A detailed producer's profit maximization model as well as a detailed non-convex independent system operator (ISO) market clearing algorithm are used. A multi-period auction is assumed, where generators, under conditions of perfect information (regarding system load and competitor offers), submit stepwise energy and reserves offer curves and, subsequently, the ISO clears the market. An innovative correction of the residual demand curve (RDC) is introduced to account for the effect of some of the non-convexities of the ISO market clearing. Convergence of the diagonalization algorithm is enhanced using reinforcement learning (RL), through which the knowledge on the success of the producers' past behavior is used to adjust the supply offers.

Optimal bidding strategy for all market players in a wholesale power market considering demand response programs

In a wholesale power market, generation companies (GENCOs) and distribution companies (DISCOs), as market players, try to maximize their payoffs by implementation of optimal strategies. This paper presents a method for determining GENCOs and DISCOs' optimal bidding strategies considering other participants' bidding and power systems operating conditions. It is assumed that DISCOs bid their distributed generations (DG) and GENCOs bid their generators in a day-ahead wholesale energy market. Meanwhile we assume that DISCOs bid interruptible loads (ILs) in market on behalf of their end customers. The proposed methodology is modeled as a bi-level multi-objective optimization problem with the upper subproblem representing individual GENCOs and DISCOs and the lower subproblem representing the Independent System Operator (ISO). The upper level maximizes the individual market participant's payoffs and the lower one solves the ISO's market clearing problem for minimizing operation costs. Genetic algorithm (GA) and fuzzy satisfying method (FSM) are employed for solving the proposed model and determining optimal bidding strategies. An eight-bus system is employed to illustrate the proposed method. Copyright © 2010 John Wiley & Sons, Ltd.

Strategic bidding in network constrained electricity markets using FAPSO

PurposeThe purpose of this paper is to develop an optimal bidding strategy for a generation company (GenCo) in the network constrained electricity markets and to analyze the impact of network constraints and opponents bidding behavior on it.Design/methodology/approachA bi‐level programming (BLP) technique is formulated in which upper level problem represents an individual GenCo payoff maximization and the lower level represents the independent system operator's market clearing problem for minimizing customers' payments. The objective function of BLP problem used for bidding strategy by economic withholding is highly nonlinear, and there are complementarity terms to represent the market clearing. Fuzzy adaptive particle swarm optimization (FAPSO), which is a modern heuristic approach, is applied to obtain the global solution of the proposed BLP problem for single hourly and multi‐hourly market clearings. Opponents' bidding behavior is modeled with probabilistic estimation.FindingsIt is very difficult to obtain the global solution of this BLP problem using the deterministic approaches, even for a single hourly market clearing. However, the effectiveness of this new heuristic approach (FAPSO) has been established with four simulation cases on IEEE 30‐bus test system considering multi‐block bidding and multi‐hourly market clearings. The joint effect of network congestion and strategic bidding by opponents offer additional opportunities of increase in payoff of a GenCo.Practical implicationsFAPSO having dynamically adjusted particle swarm optimization inertia weight uses fuzzy evaluation to effectively follow the frequently changing conditions in the successive trading sessions of a real electricity market. This approach is applied to find the optimal bidding strategy of a GenCo competing with five GenCos in IEEE 30‐bus test system.Originality/valueThis paper is possibly the first attempt to evaluate an optimal bidding strategy for a GenCo through economic withholding in a network constrained electricity market using FAPSO.

A Multiperiod Energy Acquisition Model for a Distribution Company With Distributed Generation and Interruptible Load

This paper presents a multiperiod energy acquisition model for a distribution company (Disco) with distributed generation (DG) and interruptible load (IL) in a day-ahead electricity market. Assuming that cost information for individual generation companies (Gencos) and Discos is known, the Disco's energy acquisition strategy is modeled as a bilevel optimization problem with the upper subproblem representing individual Discos and the lower subproblem representing the independent system operator (ISO). The upper subproblem maximizes individual Discos' revenues. The lower subproblem simulates the ISO's market clearing problem that minimizes generation costs and compensation costs for interrupting load. The bilevel problem is solved by a nonlinear complementarity method. An 8-bus system is employed to illustrate the proposed model and algorithm. In particular, the roles of DGs and ILs to alleviate congestion are analyzed

Efficiency of the New York independent system operator market for transmission congestion contracts

The physical nature of electricity generation and delivery creates special problems for the design of efficient markets, notably the need to manage delivery in real time and the volatile congestion and associated costs that result. Proposals for the operation of the deregulated electricity industry tend towards one of two paradigms: centralized and decentralized. Transmission congestion management can be implemented in the more centralized point‐to‐point approach, as in New York state, where derivative transmission congestion contracts (TCCs) are traded, or in the more decentralized flowgate‐based approach. While it is widely accepted that theoretically TCCs have attractive properties as hedging instruments against congestion cost uncertainty, whether efficient markets for them can be established in practice has been questioned. Based on an empirical analysis of publicly available data from years 2000 and 2001, it appears that New York TCCs provided market participants with a potentially effective hedge against volatile congestion rents. However, the prices paid for TCCs systematically diverged from the resulting congestion rents for distant locations and at high prices. The price paid for the hedge not being in line with the congestion rents, i.e., unreasonably high risk premiums are being paid, suggests an inefficient market. The low liquidity of TCC markets and the deviation of TCC feasibility requirements from actual energy flows are possible explanations.

Strategic Bidding of Transmission-Constrained GENCOs with Incomplete Information

This work describes a method for analyzing the competition among transmission-constrained generating companies (GENCOs) with incomplete information. Each GENCO models its opponents' unknown information with specific types for transforming the incomplete game into a complete game with imperfect information. The proposed methodology employs the supply function equilibrium for modeling a GENCO's bidding strategy. The competition is modeled as a bilevel problem with the upper subproblem representing individual GENCOs and the lower subproblem representing the independent system operator (ISO). The upper subproblem maximizes the individual GENCOs' payoffs and the lower subproblem solves the ISO's market clearing problem for minimizing consumers' payments. The bilevel problem is solved by developing sensitivity functions for a GENCO's payoff with respect to its bidding strategies. An eight-bus system is employed to illustrate the proposed method, and the numerical results show the impact of transfer capability on GENCOs' bidding strategies.