Day-ahead Market Operations Research Articles

Managing power balance and reserve feasibility in the AC unit commitment problem

Incorporating the AC power flow equations into unit commitment models has the potential to avoid costly corrective actions required by less accurate power flow approximations. However, research on unit commitment with AC power flow constraints has been limited to a few relatively small test networks. This work investigates large-scale AC unit commitment problems for the day-ahead market and develops decomposition algorithms capable of obtaining high-quality solutions at industry-relevant scales. The results illustrate that a simple algorithm that only seeks to satisfy unit commitment, reserve, and AC power balance constraints can obtain surprisingly high-quality solutions to this AC unit commitment problem. However, a naive strategy that prioritizes reserve feasibility leads to AC infeasibility, motivating the need to design heuristics that can effectively balance reserve and AC feasibility. Finally, this work explores a parallel decomposition strategy that allows the proposed algorithm to obtain feasible solutions on large cases within the two hour time limit required by typical day-ahead market operations.

Predicer: abstract stochastic optimisation model framework for multi-market operation

An open-source modelling framework Predicer, standing for Predictive Decider, for multi-market day-ahead market operation purposes is described in this paper. The Predicer model uses scenario-based stochastic optimisation to obtain decision variables and bid matrixes for energy and reserve markets by maximising the risk-adjusted expected value of the profit during the model time frame. The modelled energy system structure is abstract, that is, based on basic elements such as nodes representing different energy types and processes representing flows between nodes. The abstract model structure enables user to construct arbitrary energy systems and define links between assets, commodities, energy markets and reserve markets. Predicer model can include properties such as unit ramp rates, online units, dynamic energy storages, market realisation and market bidding requirements. The aggregation of unit-based energy and reserve opportunities into a virtual power plant allows the asset owner to make optimized portfolio-level bids for different market products. The model scenarios consist of user defined forecasts for market prices, renewable energy supply, energy demand and other system related time series. Predicer is implemented in Julia programming language and uses the JuMP optimisation package.

Comparative Study of the Identification Methods of the Management System of the Day-Ahead Market of Polish Energy Market S.A.

Nowadays, identification and neural methods are used more and more often in modeling IT forecasting systems in addition to analytical methods. Six characteristic models used to forecast the Day-Ahead Market system functioning as a transaction management system at the Polish Power Exchange (POLPX) and the Nord Pool Spot market have been selected for comparative analysis. The research was preceded by a detailed discussion of modern criteria used to assess the quality of model fitting to the system, namely: effectiveness, efficiency, and robustness. In the literature, there are two main groups of system modeling methods, namely time series modeling methods and identification modeling methods, including neural modeling methods. Modeling usually results in such models as parametric models and artificial neural networks learned neural models of the Day-Ahead Market, as well as time series models, among others. In the comparative analysis, special attention was paid to the accuracy of the obtained models concerning the system. It has been pointed out that the studied solutions used to measure the accuracy of modeling criteria such as accuracy of fit or efficiency, and did not use the modeling efficiency, which is very important in IT forecasting systems for such large markets as the Day-Ahead Market of POLPX. The search for the best market models, including identification models of the Day-Ahead Market operation that can be used in electricity price forecasting is a very important issue both from the point of view of algorithmic solutions and economical solutions.

The Influence of the Artificial Neural Network type on the quality of learning on the Day-Ahead Market model at Polish Power Exchange joint-stock company

The work contains the results of the Day-Ahead Market modeling research at Polish Power Exchange taking into account the numerical data on the supplied and sold electricity in selected time intervals from the entire period of its operation (from July 2002 to June 2019). Market modeling was carried out based on three Artificial Neural Network models, ie: Perceptron Artificial Neural Network, Recursive Artificial Neural Network, and Radial Artificial Neural Network. The examined period of the Day-Ahead Market operation on the Polish Power Exchange was divided into sub-periods of various lengths, from one month, a quarter, a half a year to the entire period of the market's operation. As a result of neural modeling, 1,191 models of the Market system were obtained, which were assessed according to the criterion of the least error MSE and the determination index R2.

Stochastic Optimization of Microgrid Participating Day-Ahead Market Operation Strategy with Consideration of Energy Storage System and Demand Response

More and more attention has been paid to the development of renewable energy in the world. Microgrids with flexible regulation abilities provide an effective way to solve the problem of renewable energy connected to power grids. In this article, an optimization strategy of a microgrid participating in day-ahead market operations considering demand responses is proposed, where the uncertainties of distributed renewable energy generation, electrical load, and day-ahead market prices are taken into account. The results show that, when the microgrid implements the demand response, the operation cost of the microgrid decreases by 4.17%. Meanwhile, the demand response program can transfer the peak load of the high-price period to the low-price period, which reduces the peak valley difference of the load and stabilizes the load curve. Finally, a sensitivity analysis of three factors is carried out, finding that, with the increase of the demand response adjustable ratio or the maximum capacity of the electrical storage devices, the operation cost of the microgrid decreases, while, with the increase of the demand response cost, the operation cost of the microgrid increases and, finally, tends to the cost without the demand response. The sensitivity analysis reveals that the demand response cost has a reasonable pricing range to maximize the value of the demand response.

Roth-Erev Reinforcement Learning Approach for Smart Generator Bidding towards Long Term Electricity Market Operation Using Agent Based Dynamic Modeling

This article focuses towards agent based implementation of restructured power market with learning capabilities for generators. The market model considered for the analysis is wholesale market and through learning capability of the generator will confront self-sufficient smart generator to perform optimal bidding for a long term. The Agent Based Modeling for Electricity Systems (AMES) permits dynamic testing with learning traders. The whole electricity market is managed by the Independent System Operator (ISO) which computes the hourly Locational Marginal Price (LMP) and commitments of power exchange for day-ahead market operation. The bidding strategy of generators is trained using stochastic reinforcement learning algorithm (JReLM) developed under Java platform. For the analysis, agents are classified as market traders and Independent System Operator (ISO) linking to the IEEE 5-Bus system. The analysis is further extended to an IEEE-30 Bus system and the results demonstrate great potential of the agent based computational ability in the electricity market to help the generators to exercise more market power with optimal bidding than normal generators.

Market-oriented optimal dispatching strategy for a wind farm with a multiple stage hybrid energy storage system

With the increased promotion of integrated energy power systems (IEPS), renewable energy and energy storage systems (ESS) play a more important role. However, the fluctuation and intermittent nature of wind not only results in substantial reliability and stability defects, but it also weakens the competitiveness of wind generation in the electric power market. Meanwhile, the way to further enhance the system reliability effectively improving market profits of wind farms is one of the most important aspects of Wind-ESS joint operational design. In this paper, a market-oriented optimized dispatching strategy for a wind farm with a multiple stage hybrid ESS is proposed. The first stage ESS is designed to improve the profits of wind generation through day-ahead market operations, the real-time market-based second stage ESS is focused on day-ahead forecasting error elimination and wind power fluctuation smoothing, while the backup stage ESS is associated with them to provide the ancillary service. An interval forecasting method is adopted to help to ensure reliable forecast results of day-ahead wind power, electricity prices and loads. With this hybrid ESS design, supply reliability and market profits are simultaneously achieved for wind farms.

Stochastic simulation of power systems with integrated intermittent renewable resources

Abstract We report on the development of a comprehensive, stochastic simulation methodology that provides the capability to quantify the impacts of integrated renewable resources on the power system economics, emissions and reliability variable effects over longer periods with the various sources of uncertainty explicitly represented. We model the uncertainty in the demands, the available capacity of conventional generation resources and the time-varying, intermittent renewable resources, with their temporal and spatial correlations, as discrete-time random processes. We deploy Monte Carlo simulation techniques to systematically sample these random processes and emulate the side-by-side power system and transmission-constrained day-ahead market operations. We construct the market outcome sample paths for use in the approximation of the expected values of the various metrics of interest. Our efforts to address the implementational aspects of the methodology so as to ensure computational tractability for large-scale systems over longer periods include the use of representative simulation periods, parallelization and variance reduction techniques. Applications of the approach include planning and investment studies and the formulation and analysis of policy. We illustrate the capabilities and effectiveness of the simulation approach on representative study cases on a modified WECC 240-bus system. The results provide valuable insights into the impacts of deepening penetration of wind resources.