Participation In Electricity Research Articles

Online distributed learning in wind power forecasting

Forecasting wind power generation up to a few hours ahead is of the utmost importance for the efficient operation of power systems and for participation in electricity markets. Recent statistical learning approaches exploit spatiotemporal dependence patterns among neighbouring sites, but their requirement of sharing confidential data with third parties may limit their use in practice. This explains the recent interest in distributed, privacy preserving algorithms for high-dimensional statistical learning, e.g. with auto-regressive models. The few approaches that have been proposed are based on batch learning. However, these approaches are potentially computationally expensive and do not allow for the accommodation of nonstationary characteristics of stochastic processes like wind power generation. This paper closes the gap between online and distributed optimisation by presenting two novel approaches that recursively update model parameters while limiting information exchange between wind farm operators and other potential data providers. A simulation study compared the convergence and tracking ability of both approaches. In addition, a case study using a large dataset from 311 wind farms in Denmark confirmed that online distributed approaches generally outperform existing batch approaches while preserving privacy such that agents do not have to actively share their private data.

Strategic participation in competitive electricity markets: Internal versus sectorial data analysis

Current approaches for risk management in energy market participation mostly refer to portfolio optimization for long-term planning, and stochastic approaches to deal with uncertainties related to renewable energy generation and market prices variation. Risk assessment and management as integrated part of actual market negotiation strategies is lacking from the current literature. This paper addresses this gap by proposing a novel model for decision support of players’ strategic participation in electricity market negotiations, which considers risk management as a core component of the decision-making process. The proposed approach addresses the adaptation of players’ behaviour according to the participation risk, by combining the two most commonly used approaches of forecasting in a company’s scope: the internal data analysis, and the external, or sectorial, data analysis. The internal data analysis considers the evaluation of the company’s evolution in terms of market power and profitability, while the sectorial analysis addresses the assessment of the competing entities in the market sector using a K-Means-based clustering approach. By balancing these two components, the proposed model enables a dynamic adaptation to the market context, using as reference the expected prices from competitor players, and the market price prediction by means of Artificial Neural Networks (ANN). Results under realistic electricity market simulations using real data from the Iberian electricity market operator show that the proposed approach is able to outperform most state-of-the-art market participation strategies, reaching a higher accumulated profit, by adapting players’ actions according to the participation risk.

Single and Multisettlement Approaches to Market Clearing Under Demand Uncertainty

Participants in electricity markets face a substantial amount of uncertainty, and with increased penetration of volatile renewable generation this uncertainty has further increased. Conventionally designed electricity markets cope with uncertainty by running two markets: a market that is cleared ahead of time, followed by a real-time balancing market to reconcile actual realizations of demand and available generation. In such mechanisms, the initial clearing process does not take into account the distribution of outcomes in the balancing market. Recently, an alternative so-called stochastic settlement market has been proposed (see, e.g., Pritchard et al. 2010 [Pritchard G, Zakeri G, Philpott A (2010) A single-settlement, energy-only electric power market for unpredictable and intermittent participants. Oper. Res. 58(4):1210–1219.] and Bouffard et al. 2005 [Bouffard F, Galiana FD, Conejo AJ (2005) Market-clearing with stochastic security—Part I: Formulation. IEEE Trans. Power Systems 20(4):1818–1826.]) where the ISO clears both stages in one single settlement market. While the effectiveness of the stochastic market clearing mechanism is clear when the market is competitive, this is open to question for imperfectly competitive markets. In this paper we consider simplified models for two types of market clearing mechanisms. First, a market clearing mechanism utilized in New Zealand, whereby firms offer in advance and are notified of a clearing quantity and price guide based on an estimate of demand, followed by real-time dispatch. We refer to this as NZTS. Secondly we consider a simplified stochastic programming market clearing mechanism. We compute Nash equilibria of games resulting from each of the market clearing mechanisms. We prove that under the assumption of symmetry, our simplified stochastic programming market clearing is equivalent to a two-period single settlement system that takes account of deviation penalties in the second stage. These, however, differ from NZTS. We show that when we assume symmetry, this stochastic settlement model results in better social welfare than does NZTS. We also investigate a number of asymmetric examples numerically. The e-companion is available at https://doi.org/10.1287/opre.2017.1610 .

Residual Demand Curves for Modeling the Effect of Complex Offering Conditions on Day-Ahead Electricity Markets

Residual demand curves (RDCs) can be used to represent the strategic interaction of participants in electricity markets. RDCs relate the energy that an agent can buy or sell in one hour with the clearing market price that would be obtained in such hour, assuming the market is organized as simple bid independent auctions. Despite the fact that they have been widely used in the literature, the existence of time and/or spatial constraints in the market clearing algorithm makes the RDCs not directly applicable. This paper tries to overcome these difficulties by extending the concept of RDCs to zonal pricing markets where complex offering conditions and transmission constraints are taken into account. Therefore, the RDCs are redefined in order to capture such effects, which are usually neglected or oversimplified. A new method for computing the redefined RDCs is established and its application to the Iberian electricity market is presented. The results show that modeling complex conditions and transmission constraints in RDCs can have a significant effect when compared to the standard approach found in the literature. Therefore, the method presented in this paper modeling the effect of firm's decisions on market prices in a more accurate way.

The Role of Liquidity in Electricity Markets

Starting from the premise that hedging pressure is based on limited market efficiency I provide an analogy that electricity markets over years behave like intraday stock or FX markets during the liquidation of a block trade. In this endeavour I relate the returns of some liquidity exploitation strategies with the degree of liquidity metrics by utilising the characteristic initial endowments of market participants in electricity markets. In a second step I introduce a simple stochastic model that is able to capture a variety of stylized facts of asset returns (first order price impact, Samuelson Effect, negative prices) over the whole life span of the contract and that circumvents typical information modeling pitfalls for non-storable commodities. This argument is based on modeling 'market impact' as an additional asset with a known terminal value and is justified by an enlargement of filtration argument.

Autonomous trading in modern electricity markets

The smart grid is envisioned to be a main enabler of sustainable, clean, efficient, reliable, and secure energy supply (U.S. Department of Energy, 2003). One of the milestones in the smart grid vision will be programs for customer participation in electricity markets through demand-side management and distributed generation; electricity markets will incentivize customers to adapt their demand to supply conditions, which will help to utilize intermittent energy resources such as from solar and wind, and to reduce peak-demand.

Wind farms participation in electricity markets considering uncertainties

Rising global temperature and environmental pollution as well as the demand for energy consumption have made finding new and affordable clean energy resources a serious challenge for governments. A possible solution could be renewable resources such as solar, wind or geothermal energies. Restructuring and deregulation have provided a competitive environment which makes analysis of these new energy sources necessary. Wind farms have been receiving more attention from governments because of their noticeable generation capability. The stochastic nature of the wind inflicts uncertainty on the output generation of wind farms which then causes some limitations for the participation of these farms in the electricity market. Thus, in this paper the effects of uncertainty in predicting the wind farm's power on locational marginal price in the market have been studied. According to the advantages and disadvantages of wind farm's power uncertainties, a procedure to maximize the social welfare is presented. The studies have been done on an 8-bus network for 24 h in a day-ahead electricity market. To do this, the farm power is predicted using Neural Network and Wavelet Transform and its uncertainties are calculated using the asymmetric Quantile Regression method.

A bottom-up approach for demand response aggregators’ participation in electricity markets

This paper proposes a bottom-up model for demand response (DR) aggregators in electricity markets. This model enables a DR aggregator to consider the technical constraints of customers in developing an optimal trading strategy in the wholesale electricity market. In the bottom level, DR options, called load shifting, load curtailment and load recovery are comprehensively modelled in a stochastic programming approach. Each DR program is mathematically formulated in such a way that practically models the constraints of customers. Further, the proposed model considers the customers’ behaviour in participating in the given DR program through a scenario-based participation factor. On the other hand, the upper level proposes trading the DR outcome in day-ahead and balancing markets with uncertain prices, as well as in forward contracts with a predefined price. The overall bottom-up problem is formulated as a stochastic profit maximization model for the DR aggregator, in which the risk is taken into account using the Conditional Value-at-Risk (CVaR) measure. The feasibility of the given strategy is assessed on a case of the Nordic market.

Impact of network payment schemes on transmission expansion planning with variable renewable generation

A large number of studies have dealt with the Transmission Expansion Planning (TEP) problem. However, few investigations have focused on analyzing the impacts of network payment schemes on network configuration and the benefits/losses distribution among the participants in electricity markets. In this paper, we propose a multi-annual transmission expansion planning model considering four different network payment schemes to finance the construction of new transmission lines, seeking to reduce the total system costs. Wind and solar power generation are included in the model taking into account their variability. The proposed models are reformulated as Mixed Integer Linear Programming (MILP) problems. We use seven performance metrics related with congestion, nodal prices and generator benefits, among others, to evaluate the effect of each payment scheme. A realistic case study based on the main power system in Chile is analyzed to illustrate the proposed models. It is shown that integrating line cost-recovering equations into the TEP model may result into a more realistic and less congested power network. Also, total system cost is highly related with transmission tariff discrimination. In that way, tariffs with high location dependence perform better in the case studied, the Chilean power system.

Electricity price forecasting using a new data fusion algorithm

Accurate price forecasting is crucial for all market participants in electricity markets. This study presents a hybrid price forecasting framework based on a new data fusion algorithm. Owing to the complexity and distinct nature of the electricity price, a single forecast engine cannot capture all different patterns of the price signals. Hence, this study focuses on a hybrid forecasting method to extract the advantages of several forecasting engines. In the proposed method, artificial neural network, adaptive neuro-fuzzy inference system and autoregressive moving average are employed as primary forecast engines (agents) which provide three independent price forecasts. Then, a new data fusion algorithm, the modified ordered weighted average (modified OWA), is proposed to combine the three forecasts to generate a single unified price forecast. Hopefully, the fusion's output outperforms all the agents’ forecasts. The author's proposed fusion algorithm, unlike conventional OWA, uses the feedback from the agents’ error. The proposed framework is evaluated on the Spanish electricity market. The results confirm the ability of the proposed fusion framework to provide more accurate forecasts compared with the input agents forecasts. Results are also compared with some of the recent electricity price forecasting methods.

A hybrid application algorithm based on the support vector machine and artificial intelligence: An example of electric load forecasting

Accurate electric load forecasting could prove to be a very useful tool for all market participants in electricity markets. Because it can not only help power producers and consumers make their plans but also can maximize their profits. In this paper, a new combined forecasting method (ESPLSSVM) based on empirical mode decomposition, seasonal adjustment, particle swarm optimization (PSO) and least squares support vector machine (LSSVM) model is proposed. In the electric market, noise signals usually affect the forecasting accuracy, which were caused by different erratic factors. First of all, ESPLSSVM uses an empirical mode decomposition-based signal filtering method to reduce the influence of noise signals. Secondly, ESPLSSVM eliminates the seasonal components from the de-noised resulting series and then it models the resultant series using the LSSVM which is optimized by PSO (PLSSVM). Finally, by multiplying the seasonal indexes by the PLSSVM forecasts, ESPLSSVM acquires the final forecasting result. The effectiveness of the presented method is examined by comparing with different methods including basic LSSVM (LSSVM), empirical mode decomposition-based signal filtering method processed by LSSVM (ELSSVM) and seasonal adjustment processed by LSSVM (SLSSVM). Case studies show ESPLSSVM performed better than the other three load forecasting approaches.

Stochastic security constrained unit commitment incorporating demand side reserve

Increasing the social welfare and achieving real competitive markets are the main purposes of consumers participation in electricity markets. Hence, Demand Response Programs (DRPs) have been designed to consider the consumers participation. One of these programs named Emergency Demand Response Program (EDRP) is based on consumers’ responses to high electricity prices and to the incentives that are paid by Independent System Operators (ISOs) in the critical hours. In this paper, a model has been proposed for EDRP participation in the Stochastic Security Constrained Unit Commitment (SCUC) program. Both the spinning reserve and the demand side reserve, which is modeled as EDRP, are taken into account as the operating reserve services. The effects of EDRP and its parameters on the system operation cost have been investigated under power system uncertainties. The two-stage stochastic SCUC model has been utilized for simultaneous clearing of energy and reserve markets. Mixed Integer Linear Programming (MILP) has been used for modeling the proposed method in the GAMS (General Algebraic Modeling System) environment. The proposed model is applied to a 6-bus test system and a modified IEEE Reliability Test System (RTS) to demonstrate its effectiveness.

Issues and solution approaches in PHEV integration to smart grid

Abstract Successful integration of plug-in hybrid electric vehicles (PHEVs) into the power system is a major challenge for the future smart grid. As a result, this topic has attracted the attention of researchers from different communities in the past few years. Recent research progress has addressed different technical aspects of PHEV integration into the smart grid; such as charging and control strategies of PHEVs, vehicle-to-grid (V2G) technology, and several application domains, such as wind energy integration, frequency regulation, design of parking areas and participation in electricity markets. Most of these works require formulation of mathematical models which extensively use artificial intelligence techniques, intelligent methods and agent-based computing approaches to solve the computational problems. This paper presents a comprehensive survey of different research problems and their solution approaches in the context of PHEV integration to smart grid.

Benefits for Wind Energy in Electricity Markets from Using Short Term Wind Power Prediction Tools; A Simulation Study

One of the characteristics of wind energy, from the grid point of view, is its non-dispatchability, i.e. generation cannot be ordered, hence integration in electrical networks may be difficult. Short-term wind power prediction-tools could make this integration easier, either by their use by the grid System Operator, or by promoting the participation of wind farms in the electricity markets and using prediction tools to make their bids in the market. In this paper, the importance of a short-term wind power-prediction tool for the participation of wind energy systems in electricity markets is studied. Simulations, according to the current Spanish market rules, have been performed to the production of different wind farms, with different degrees of accuracy in the prediction tool. It may be concluded that income from participation in electricity markets is increased using a short-term wind power prediction-tool of average accuracy. This both marginally increases income and also reduces the impact on system operation with the improved forecasts.