Day-ahead Electricity Market Research Articles

Wind Power with Energy Storage Arbitrage in Day-ahead Market by a Stochastic MILP Approach

Abstract This paper is about a support information management system for a wind power (WP) producer having an energy storage system (ESS) and participating in a day-ahead electricity market. Energy storage can play not only a leading role in mitigation of the effect of uncertainty faced by a WP producer, but also allow for conversion of wind energy into electric energy to be stored and then released at favourable hours. This storage provides capability for arbitrage, allowing an increase on profit of a WP producer, but must be supported by a convenient problem formulation. The formulation proposed for the support information management system is based on an approach of stochasticity written as a mixed integer linear programming problem. WP and market prices are considered as stochastic processes represented by a set of scenarios. The charging/discharging of the ESS are considered dependent on scenarios of market prices and on scenarios of WP. The effectiveness of the proposed formulation is tested by comparison of case studies using data from the Iberian Electricity Market. The comparison is in favour of the proposed consideration of stochasticity.

Optimizing Day-Ahead Electricity Market Prices: Increasing the Total Surplus for Energy Exchange Istanbul

Problem definition: We design a combinatorial auction to clear the Turkish day-ahead electricity market, and we develop effective tabu search and genetic algorithms to solve the problem of matching bidders and maximizing social welfare within a reasonable amount of time for practical purposes. Academic/practical relevance: A double-sided blind combinatorial auction is used to determine electricity prices for day-ahead markets in Europe. Considering the integer requirements associated with market participants’ bids and the nonlinear social welfare objective, a complicated problem arises. In Turkey, the total number of bids reaches 15,000, and this large problem needs to be solved within minutes every day. Given the practical time limit, solving this problem with standard optimization packages is not guaranteed, and therefore, heuristic algorithms are needed to quickly obtain a high-quality solution. Methodology: We use nonlinear mixed-integer programming and tabu search and genetic algorithms. We analyze the performance of our algorithms by comparing them with solutions commercially available to the market operator. Results: We provide structural results to reduce the problem size and then develop customized heuristics by exploiting the problem structure in the day-ahead market. Our algorithms are guaranteed to generate a feasible solution, and Energy Exchange Istanbul has been using them since June 2016, increasing its surplus by 448,418 Turkish liras (US$128,119) per day and 163,672,570 Turkish liras (US$46,763,591) per year, on average. We also establish that genetic algorithms work better than tabu search for the Turkish day-ahead market. Managerial implications: We deliver a practical tool using innovative optimization techniques to clear the Turkish day-ahead electricity market. We also modify our model to handle similar European day-ahead markets and show that performances of our heuristics are robust under different auction designs.

Should environmental constraints be considered in linear programming based water value calculators?

We evaluate the practical usefulness of incorporating maximum ramping rates and minimum environmental flows into a linear programming based water value calculator for hydropower plants that participate in the day-ahead electricity market. The methodology consists of three steps: first computing the water value once with and once without environmental constraints, then simulating the plant operations using each water value, and finally comparing the simulation profits. A set of nine representative hydropower plants formed by combinations of three real locations (in Colombia, Norway and Spain) and three turbine configurations (from one to three Francis units) are individually analyzed. Each plant is simulated in two synthetic 10-year long series subject to fifteen combinations of maximum ramping rates and minimum flows with the two above-mentioned water values, totaling 540 simulations. The results indicate that incorporating the analyzed environmental constraints into a linear programming based water value calculator can be significantly profitable only when the hydropower plants have only one or at most two turbines.

The cannibalization effect of wind and solar in the California wholesale electricity market

Increasing penetration of zero marginal cost variable renewable technologies cause the decline of wholesale electricity prices due to the merit-order effect. This causes a “cannibalization effect” through which increasing renewable technologies’ penetration undermines their own value. We calculate solar and wind daily unit revenues (generation weighted electricity prices) and value factors (unit revenues divided by average electricity prices) from hourly data of the day-ahead California wholesale electricity market (CAISO) for the period January 2013 to June 2017. We then perform a time series econometric analysis to test the absolute (unit revenues) and relative (value factors) cannibalization effect of solar and wind technologies, as well as the cross-cannibalization effects between technologies. We find both absolute and relative cannibalization effect for both solar and wind, but while wind penetration reduces the value factor of solar, solar penetration increases wind value factor, at least at high penetration and low consumption levels. We explore non-linearities and also find that the cannibalization effect is stronger at low consumption and high wind/solar penetration levels. This entails that wind and (mainly) solar competitiveness could be jeopardized unless additional mitigation measures such as storage, demand management or intercontinental interconnections are taken.

Two‐stage stochastic operation framework for optimal management of the water–energy–hub

This study proposes a bi-level stochastic framework to address optimal scheduling of energy hub (EH) in a pool-based short-term market considering electrical–thermal–water demands. EH acts as an independent price-maker producer in a day-ahead electricity market aiming to maximise its profit. The market settlement mechanism is constructed as the pay-at-market-clearing price (MCP), where each producer/consumer is paid at the MCP. The problem model is formulated as a bi-level optimisation approach in a stochastic environment, in which the upper level defines the profit maximisation of the proposed strategic producer, whereas the lower-level expresses the dispatch cost of the considered power grid. This results in a problem formulation with mathematical equilibrium constraints which is transformed into a new mixed-integer linear programme based on Karush–Kuhn–Tucker conditions. A stochastic framework based on unscented transform is developed to model the high uncertainties of EH water demand, EH thermal demand, EH electric demand, generators and loads submitted price to the market. The simulation results on the IEEE test system advocate the effectiveness and appropriate performance of the proposed strategic EH producer in the electricity market and its effect on the locational marginal prices of buses in a transmission-constrained market.

Merit Order Effect Modeling: The Case of the Hellenic Electricity Market

A simple effective model is proposed for the day-ahead electricity market. The model considers the main factors which govern the process, predicts the seasonal and daily variation of electricity demand, renewable production, system marginal price, and merit order effect. The accuracy of the model is increased by fitting to historic data of the Hellenic electricity market. During the period between October 2016 and December 2018, the Hellenic electricity market calculated explicitly the merit order effect using an innovative mechanism to directly charge the electricity suppliers (retailers). On the basis of the proposed model and the market recorded data, the effect of the renewable penetration on the wholesale Hellenic electricity prices is revealed. The model is further used to analyze the market future behavior when basic factors (electricity demand, conventional power, and renewable penetration) are known or estimated. The effect of merit order effect on the Hellenic legislation is discussed and the appropriate measures adopted by the Hellenic authorities are analyzed and evaluated.

IGDT-Based Wind–Storage–EVs Hybrid System Robust Optimization Scheduling Model

Wind power has features of uncertainty. When wind power producers (WPPs) bid in the day-ahead electricity market, how to deal with the deviation between forecasting output and actual output is one of the important topics in the design of electricity market with WPPs. This paper makes use of a non-probabilistic approach—Information gap decision theory (IGDT)—to model the uncertainty of wind power, and builds a robust optimization scheduling model for wind–storage–electric vehicles(EVs) hybrid system with EV participations, which can make the scheduling plan meet the requirements within the range of wind power fluctuations. The proposed IGDT robust optimization model first transforms the deterministic hybrid system optimization scheduling model into a robust optimization model that can achieve the minimum recovery requirement within the range of wind power output fluctuation, and comprehensively considers each constraint. The results show that the wind–storage–EVs hybrid system has greater operational profits and less impact on the safe and stable operation of power grids when considering the uncertainty of wind power. In addition, the proposed method can provide corresponding robust wind power fluctuation under different expected profits of the decision-maker to the wind–storage–EVs hybrid system.

On the Interaction Between Aggregators, Electricity Markets and Residential Demand Response Providers

To decarbonize the heating sector, residential consumers may install heat pumps. Coupled with heating loads with high thermal inertia, these thermostatically controlled loads may provide a significant source of demand side flexibility. Since the capacity of residential consumers is typically insufficient to take part in the day-ahead electricity market (DAM), aggregators act as mediators that monetize the flexibility of these loads through demand response (DR). In this paper, we study the strategic interactions between an aggregator, its consumers and the DAM using a bilevel optimization framework. The aggregator-consumer interaction is captured either as a Stackelberg or a Nash Bargaining Game, leveraging chance-constrained programming to model limited controllability of residential DR loads. The aggregator takes strategic positions in the DAM, considering the uncertainty on the market outcome, represented as a stochastic Stackelberg Game. Results show that the DR provider-aggregator cooperation may yield significant monetary benefits. The aggregator cost-effectively manages the uncertainty on the DAM outcome and the limited controllability of its consumers. The presented methodology may be used to assess the value of DR in a deregulated power system or may be directly integrated in the daily routine of DR aggregators.

Bidding strategy for generators considering ramp rates in a day-ahead electricity market

In a day-ahead electricity market, competitive bidding strategy plays a vital role for power suppliers to maximize their profit. In this type of market, each power supplier submits a set of hourly production prices and offers capacity for the next period. The market operator, after receiving this data along with forecasted hourly load from the demand side, allocates production output to each unit. Power suppliers face the problem in trading their offers in the market, due to the uncertain behavior of competitive power suppliers and power demand. Therefore, the power supplier requires a suitable bidding strategy for handling uncertainty in the market to maximize their profits. Moreover, the considerations of ramp rates are necessary for the precise representation of practical power system. Thus, in the present work, a modified gravitational search algorithm based on oppositional learning concept is used to solve strategic bidding problem for power suppliers considering six generators with ramp rates, 24-h load data and rivals behavior. The total hourly profits of generators with and without considering ramp rates have been compared.

Learning From Past Bids to Participate Strategically in Day-Ahead Electricity Markets

We consider the process of bidding by electricity suppliers in a day-ahead market context, where each supplier bids a linear non-decreasing function of her generating capacity with the goal of maximizing her individual profit given other competing suppliers’ bids. Based on the submitted bids, the market operator schedules suppliers to meet demand during each hour and determines hourly market clearing prices. Eventually, this game-theoretic process reaches a Nash equilibrium when no supplier is motivated to modify her bid. However, solving the individual profit maximization problem requires information of rivals’ bids, which are typically not available. To address this issue, we develop an inverse optimization approach for estimating rivals’ production cost functions given historical market clearing prices and production levels. We then use these functions to bid strategically and compute Nash equilibrium bids. We present numerical experiments illustrating our methodology, showing good agreement between bids based on the estimated production cost functions with the bids based on the true cost functions. We discuss an extension of our approach that takes into account network congestion resulting in location-dependent prices.

A novel bidding method for combined heat and power units in district heating systems

We propose a bidding method for the participation of combined heat and power (CHP) units in the day-ahead electricity market. More specifically, we consider a district heating system where heat can be produced by CHP units or heat-only units, e.g., gas or wood chip boilers. We use a mixed-integer linear program to determine the optimal operation of the portfolio of production units and storages on a daily basis. Based on the optimal production of subsets of units, we can derive the bidding prices and amounts of electricity offered by the CHP units for the day-ahead market. The novelty about our approach is that the prices are derived by iteratively replacing the production of heat-only units through CHP production. This results in an algorithm with a robust bidding strategy that does not increase the system costs even if the bids are not won. We analyze our method on a small realistic test case to illustrate our method and compare it with other bidding strategies from literature, which consider CHP units individually. The analysis shows that considering a portfolio of units in a district heating system and determining bids based on replacement of heat production of other units leads to better results.

Improvement in social welfare division among buyer and supplier in LP based market clearing method in day-ahead electricity market

ABSTRACTMost Electric Power Exchange (PX) around the world employs Linear Programming (LP) based method for step-wise market clearing in Day-Ahead Electricity Market (DAM). Market clearing involves calculation of Market Clearing Volume (MCV) and Market Clearing Price (MCP) for various time blocks. LP based method gives accurate results when there exists unique intersection point between cumulative supply and cumulative demand curve. It requires additional information to handle multiple intersection points. But MCP obtained using LP based method leads to an unfair social welfare division among suppliers and buyers. Therefore, this paper investigates all possible cases where there exists an unfair social welfare division among suppliers and buyers. Modification to the existing LP based market clearing method to obtain that market equilibrium point which results in fair social welfare division is also discussed. The performance of the proposed method to obtain MCV & MCP which ensures fair social welfare division between suppliers and buyers under six different cases is tested using a simple test problem and DAM data obtained from Indian Energy Exchange (IEX) websites. Results clearly show that the proposed method is capable enough to generate market equilibrium which results in fair social welfare division among buyers and suppliers.Abbreviations: PX - Electric Power Exchange; LP - Linear Programming; DAM - Day Ahead Electricity Market; MCV - Market Clearing Volume; MCP - Market Clearing Price; IEX - Indian Energy Exchange; USD - United States Dollar; REC - Renewable Energy; TAM - Term Ahead Market; CERC - Central Electricity Regulatory Commission

Portfolio Diversification for an Intermediary Energy Storage Merchant

Deploying a certain capacity of storage assets in a renewable-only generation portfolio is unarguably imperative to maintain system reliability. These storage assets can be owned and managed by utilities, renewable energy developers, system operator, or any private merchant. Intermediary storage merchant can invest on these storage assets through capacity contracts. Investing on capacity contracts is risky owing to the inherent uncertainties in the energy market and renewable energy generation. By contracting a selected set of geographically diverse storage assets, the merchant can mitigate the risk of revenue loss and improve the return profile. This paper aims to determine the optimal storage portfolio for the merchant from a risk averse perspective. We formulate this problem as a tri-level stochastic optimization program. The upper level minimizes the aggregate risk for the intermediary merchant, the mid-level maximizes the expected profit for the owner of the non-contracted storage assets, and the lower level clears a day-ahead electricity market. The proposed tri-level problem is reformulated as a bi-level program and solved by column-and-constraint generation technique. The experiments are conducted on an IEEE 24-bus and 96-bus systems, and the generation is assumed to be wind and solar only. The impact of regional and geographical diversification on the created portfolios is studied.

Demand Response of an Industrial Buyer considering Congestion and LMP in Day-Ahead Electricity Market

Abstract This paper proposes a methodology for developing Price Responsive Demand Shifting (PRDS) based bidding strategy of an industrial buyer, who can reschedule its production plan, considering power system network constraints. Locational Marginal Price (LMP) methodology, which is being used in PJM, California, New York, and New England electricity markets, has been utilized to manage the congestion. In this work, a stochastic linear optimization formulation comprising of two sub-problems has been proposed to obtain the optimal bidding strategy of an industrial buyer considering PRDS bidding. The first sub-problem is formulated as to maximize the social welfare of market participants subject to operational constraints and security constraints to facilitate market clearing process, while the second sub-problem represents the industrial buyer’s purchase cost saving maximization. The PRDS based bidding strategy, which is able to shift the demand, from high price periods to low price periods, has been obtained by solving two subproblems. The effectiveness of the proposed method has been tested on a 5-bus system and modified IEEE 30-bus system considering the hourly day-ahead market. Results obtained with the PRDS based bidding strategy have been compared with those obtained with a Conventional Price-Quantity (CPQ) bid. In simulation studies, it is observed that the PRDS approach can control the LMPs and congestion at the system buses. It is also found that PRDS can mitigate the market power by flattening the demand, which led to more saving and satisfying demand.

Supply function Nash equilibrium of joint day-ahead electricity markets and forward contracts

Forward contracts are one of the prevalent and useful tools for managing the risks associated with the volatility of the electricity market prices. Forward contracts and day-ahead electricity market are executed simultaneously, and hence, they affect each other. This paper proposes a comprehensive supply function equilibrium model to consider the mutual interactions between forward contracts and the associated day-ahead electricity market in a power system. Negotiation between each producer and consumer in forward market is taken into account in the model. In order to consider the risk management behaviors of all market players in the model, a new risk management method is presented. The proposed risk management method takes into account the concerns of market players about the future prices of day-ahead market over the delivery period. The model proposed in this paper is applied to a test system with forward and day-ahead markets. The results are compared with the case that there is no forward contract in the power system. Impacts of growing the concerns of producers and consumers about the future, impacts of increasing demand uncertainty, impacts of improving bargaining power of consumers in contracting period and impact of contracting obligations for producers on the simulation results are discussed. The proposed risk management method is compared with CVaR method and its efficiency is evaluated. Finally, applicability of the proposed model to real size power systems is examined.

Optimal Re-Dispatching of Cascaded Hydropower Plants Using Quadratic Programming and Chance-Constrained Programming

Stochastic production from wind power plants imposes additional uncertainty in power system operation. It can cause problems in load and generation balancing in the power system and can also cause congestion in the transmission network. This paper deals with the problems of congestion in the transmission network, which are caused by the production of wind power plants. An optimization model for corrective congestion management is developed. Congestions are relieved by re-dispatching several cascaded hydropower plants. Optimization methodology covers the optimization period of one day divided into the 24 segments for each hour. The developed optimization methodology consists of two optimization stages. The objective of the first optimization stage is to obtain an optimal day-ahead dispatch plan of the hydropower plants that maximizes profit from selling energy to the day-ahead electricity market. If such a dispatch plan, together with the wind power plant production, causes congestion in the transmission network, the second optimization stage is started. The objective of the second optimization stage is the minimization of the re-dispatching of cascaded hydropower plants in order to avoid possible congestion. The concept of chance-constrained programming is used in order to consider uncertain wind power production. The first optimization stage is defined as a mixed-integer linear programming problem and the second optimization stage is defined as a quadratic programming (QP) problem, in combination with chance-constrained programming. The developed optimization model is tested and verified using the model of a real-life power system.

Uncertainty-Based Models for Optimal Management of Energy Hubs Considering Demand Response

Energy hub (EH) is a concept that is commonly used to describe multi-carrier energy systems. New advances in the area of energy conversion and storage have resulted in the development of EHs. The efficiency and capability of power systems can be improved by using EHs. This paper proposes an Information Gap Decision Theory (IGDT)-based model for EH management, taking into account the demand response (DR). The proposed model is applied to a semi-realistic case study with large consumers within a day ahead of the scheduling time horizon. The EH has some inputs including real-time (RT) and day-ahead (DA) electricity market prices, wind turbine generation, and natural gas network data. It also has electricity and heat demands as part of the output. The management of the EH is investigated considering the uncertainty in RT electricity market prices and wind turbine generation. The decisions are robust against uncertainties using the IGDT method. DR is added to the decision-making process in order to increase the flexibility of the decisions made. The numerical results demonstrate that considering DR in the IGDT-based EH management system changes the decision-making process. The results of the IGDT and stochastic programming model have been shown for more comprehension.

Scenario-Based Economic Dispatch With Uncertain Demand Response

This paper introduces a new computational framework to account for uncertainties in day-ahead electricity market clearing process in the presence of demand response providers. A central challenge when dealing with many demand response providers is the uncertainty of its realization. In this paper, a new economic dispatch framework that is based on the recent theoretical development of the scenario approach is introduced. By removing samples from a finite uncertainty set, this approach improves dispatch performance while guaranteeing a quantifiable risk level with respect to the probability of violating the constraints. The theoretical bound on the level of risk is shown to be a function of the number of scenarios removed. This is appealing to the system operator for the following reasons: (1) the improvement of performance comes at the cost of a quantifiable level of violation probability in the constraints; (2) the violation upper bound does not depend on the probability distribution assumption of the uncertainty in demand response. Numerical simulations on (1) 3-bus and (2) IEEE 14-bus system (3) IEEE 118-bus system suggest that this approach could be a promising alternative in future electricity markets with multiple demand response providers.

Improving the profitability of anaerobic digestion: is the public support framework compatible with participation in the day-ahead electricity market?

There is an increasing need for an alternative energy source within the electricity market to counterbalance the intermittent supply from renewable energy sources as their share in the market continues to grow. Anaerobic digestion could play a role in fulfilling this need through participation in the day-ahead electricity market. The potential for anaerobic digestion operators in Flanders, Belgium, and the impact of the current Flemish support policy framework on this potential are investigated by developing a non-linear optimization model that maximizes profits for the complete anaerobic digestion system, taking into account the current policy framework and a proposed alternative one. Our analysis indicates that there is a potential profit to be made for anaerobic digestion operators in the day-ahead market. However, the current framework of public support is not conducive to their participation. By allowing the same amount of support to be spread over a longer period of time, the use of flexible electricity production in the anaerobic digestion sector could be stimulated, and the profitability of this sector increased.

Day-ahead stochastic electricity market clearing considering renewable power uncertainty

The electricity market development in China faces a new background of increasing proportion of renewable power. Different from conventional power plants, the output of renewable power plants usually has strong uncertainty, which is a challenge for electricity market clearing strategy. This paper proposes a day-ahead electricity market clearing method that considers the uncertainty of renewable power sources, which is modelled based on the truncated versatile distribution. The scheduled power of conventional power plants and renewable power plants is determined by minimizing the social cost. Case studies show that the proposed method can greatly reduce the total social cost.