Day-ahead Power Market Research Articles

An electricity-carbon joint market optimization model for coal-fired power system under China's “dual-carbon” target

In the process of low-carbon transformation of power system in China, providing enough flexible service from coal-fired power units (CFPUs) is one of the most crucial answers to reduce or even avoid the operation risk which is caused by large-scale expansion of variable renewable energy (VRE). To spur CFPUs release flexibility, this paper suggests a two-stage optimal model, taking into account various peaking compensation mechanisms (VPCM) in a day-ahead power and carbon emissions trade (CET) joint market. Pre-clearing is the first stage, which maximizes social welfare, and formal clearing is the second stage, which maximizes the profit of the coal-fired power system. In contrast to the existing fixed peaking compensation mechanism (FPCM), the variable and fixed costs associated with providing peaking regulation are calculated by the VPCM. In addition, the work considers the CET market as an external carbon emissions constraint and internal constraint and no constraint are set to study the effectiveness of the current CET market. The result indicates that the VPCM can adapt more successfully to the cost of CFPUs when providing peaking regulation and increase the profit of the coal-fired power system. Then, “VPCM + external carbon constraint” enables larger profits for coal-fired power systems, whereas “FPCM + internal carbon constraint” stimulates VRE integration and reduces carbon emissions. Finally, the current CET market mechanism is insufficient to support coal-fired power plants in reducing carbon emissions as VRE rises.

A trading optimization model for virtual power plants in day-ahead power market considering uncertainties

Background: The day-ahead power market is an important part of the spot market. In the day-ahead market, participants make short-term forecasts of the load and output to propose the bidding curve more precisely. As energy aggregators that have regulatory resources, virtual power plants (VPPs) need to consider the uncertainty of distributed renewable energy output when participating in power market transactions.Methods: This paper analyzes the uncertainty and built an optimization model for VPP in day-ahead power market considering the uncertainty from both inner parts and the market environment. To verify the model, a simulation study is ran.Results: And the study results show the following: 1) the forecasting model is more efficient than the traditional algorithm in terms of accuracy, and 2) the confidence levels are not fully positive with the benefit of VPPs.Discussion: Improving the confidence level could reduce the uncertainty brought by renewable energy, but could also cause conservative trading behavior and affect the consumption of renewable energy.

Optimizing Power Demand Side Response Strategy: A Study Based on Double Master–Slave Game Model of Multi-Objective Multi-Universe Optimization

In the pilot provinces of China’s electricity spot market, power generation companies usually adopt the separate bidding mode, which leads to a low willingness of demand-side response and poor flexibility in the interaction mechanism between supply and demand. Based on the analysis of the demand response mechanism of the power day-ahead market with the participation of power sales companies, this paper abstracted the game process of the “power grid-sales company-users” tripartite competition in the electricity market environment into a two-layer (purchase layer/sales layer) game model and proposed a master–slave game equilibrium optimization strategy for the day-ahead power market under the two-layer game. The multi-objective multi-universe optimization algorithm was used to find the Pareto optimal solution of the game model, a comprehensive evaluation was constructed, and the optimal strategy of the demand response was determined considering the peak cutting and valley filling quantity of the power grid, the profit of the electricity retailers, the cost of the consumers, and the comfort degree. Examples are given to simulate the day-ahead electricity market participated in by the electricity retailers, analyze and compare the benefits of each market entity participating in the demand response, and verify the effectiveness of the proposed model.

A New Robust Bidding Approach for Wind Power Producers Participating in Competitive Power Markets with Correlated Market Prices

In this research, a bidding problem for a wind-power plant participating in a day-ahead power market with uncertain correlated market prices is studied. A new robust optimization approach considering correlation among uncertainty on the hourly prices in a day-ahead market is developed. This results in solutions with lower level of over-conservatism. For this purpose a new correlated polyhedral uncertainty set is introduced. To consider the uncertainty of market clearing prices and the value of power produced by wind power producer a bidding algorithm is developed. Results of the study using a robust modelling the bidding problem reveal that the appliance of the proposed model on the bidding problem for a price-taker wind power plant in a day-ahead market with uncertain correlated data leads to solutions with superior performance than that of the conventional polyhedral uncertainty sets.

Bidding behaviors of GENCOs under bounded rationality with renewable energy

With the worldwide restructuring of power markets, it is more meaningful to analyze the bidding behaviors of generation companies (GENCOs). A realistic bidding behavior model can help to design better power market mechanisms. However, most existing studies usually use strong assumptions, where all GENCOs behave perfectly rationally when participating in power markets. Many GENCOs' observed bidding behaviors are proved to deviate from the simulated perfectly rational ones. Thus, it is significant to introduce individual subjective perspectives into the bidding behavior modeling to obtain more realistic models. Although several studies have been working on this topic by introducing bounded rationality, they still have some problems in modeling bounded rationality, power system operation, power market rules, etc. This paper proposes a novel bidding behavior model for GENCOs under bounded rationality with renewable energy to overcome these problems. The introduced bounded rationality features include prospect theory in the form of framing effects and the concept of fairness constraints on profit-making. A Stackelberg game model is formulated to analyze the interaction between a GENCO with renewables and a day-ahead power market. The proposed model is nonlinear, which is further transformed into a mixed-integer linear formulation by applying Karush–Kuhn–Tucker theorem and strong duality theorem. Based on a modified IEEE-30 bus system with high renewable, an illustrative example is proposed to demonstrate the effectiveness of the proposed model and analyze the bidding behaviors of bounded rational GENCOs and the corresponding effects on power markets.

Information gap-based scheduling strategy of a multi-energy retailer with integrated demand response program

• Modeling a multi-energy retailer in the framework of multi-carrier energy systems. • Developing a multi-energy retailer based on gas/cooling/heating/power carriers. • Presenting an IGDT method to cope with uncertainties in the optimization model. • Integrating flexible options, e.g. hybrid energy storage in the optimization model. • Proposing integrated DR to control multiple energy demands simultaneously. Multi-energy systems (MESs) were introduced to enhance the flexibility and efficiency of conventional energy distribution systems. In this new trend, it is possible to supply different energy carriers simultaneously by the multi-energy retailer (MER), under which, without the need to transfer all of them to different locations, the loads are fed centrally, and the customers can purchase all the carriers they need from the desired MER. Motivated by these descriptions, this paper focuses on the optimal scheduling of MER in the integrated energy system, including natural gas, electricity, cooling, and heating, under the information gap decision theory (IGDT) framework. The MER's goal is to maximize profits by considering the uncertainty of the day-ahead power market. The proposed IGDT approach without a probabilistic distribution function analyzes the risk-aversion strategy associated with electricity price uncertainty. In this paper, integrated demand response (IDR) for natural gas, electrical, heating, and cooling loads are developed simultaneously as a flexible demand-side management resource. The proposed model contains multiple technologies owned by MER, including hybrid energy storage technology, combined heat and power, chiller, boiler units, and renewable resources. Numerical results from different cases illustrate the effectiveness of IDR installation in terms of profitability. Besides, the MER can serve multiple demands more efficiently. Under the IGDT approach, the purchasing power cost is reduced up to 24.23%.

Impact of Time-of-Use Demand Response Program on Optimal Operation of Afghanistan Real Power System

Like most developing countries, Afghanistan still employs the traditional philosophy of supplying all its load demands whenever they happen. However, to have a reliable and cost-effective system, the new approach proposes to keep the variations of demand at the lowest possible level. The power system infrastructure requires massive capital investment; demand response (DR) is one of the economic options for running the system according to the new scheme. DR has become the intention of many researchers in developed countries. However, very limited works have investigated the employment of appropriate DR programs for developing nations, particularly considering renewable energy sources (RESs). In this paper, as two-stage programming, the effect of the time-of-use demand response (TOU-DR) program on optimal operation of Afghanistan real power system in the presence of RESs and pumped hydropower storage (PHS) system in the day-ahead power market is analyzed. Using the concept of price elasticity, first, an economic model indicating the behaviour of customers involved in TOU-DR program is developed. A genetic algorithm (GA) coded in MATLAB software is used accordingly to schedule energy and reserve so that the total operation cost of the system is minimized. Two simulation cases are considered to verify the effectiveness of the suggested scheme. The first stage programming approach leads case 2 with TOU-DR program to 35 MW (811 MW − 776 MW), $16,235 ($528,825 − $512,590), and 64 MW reductions in the peak load, customer bill and peak to valley distance, respectively compared to case 1 without TOU-DR program. Also, the simulation results for stage 2 show that by employing the TOU-DR program, the system’s total cost can be reduced from $317,880 to $302,750, which indicates a significant reduction in thermal units’ operation cost, import power tariffs and reserve cost.

An economic risk based optimal bidding strategy for various market players considering optimal wind placements in day-ahead and real-time competitive power market

An economic risk based optimal bidding strategy for various market players considering optimal wind placements in day-ahead and real-time competitive power market

Unit Commitment with Ancillary Services in a Day-Ahead Power Market

This paper integrates Discrete Particle Swarm Optimization (DPSO) and Sequential Quadratic Programming (SQP) to propose a DPSO-SQP method for solving unit commitment problems for ancillary services. Through analysis of ancillary services, including Automatic Generation Control (AGC), Real Spinning Reserve (RSR), and Supplemental Reserve (SR), the cost model of unit commitment was developed. With the requirements of energy balance, ancillary services, and operating constraints considered, DPSO-PSO was used to calculate the energy supply of each source, including the associated AGC, RSR, and SR, and the operating cost of a day-ahead power market was calculated. A study case using the real data from thermal units of Taipower Company (TPC) and Independent Power Producers (IPPs) demonstrated effective results for the “summer” and “non-summer” seasons, as classified by TPC for the two charging rates. According to the test cases in this research, costs without ancillary services in non-summer and summer seasons are higher than those with ancillary services. The simulation results are also compared with the Genetic Algorithm (GA), Evolutionary Programming (EP), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). DPSO-PSO shows effectiveness in solving unit commitment problems with enhanced sorting efficiency, and a higher probability of reaching the global optimum.

A Robust Optimization Technique for Energy Cost Minimization of Cloud Data Centers

Power consumption cost constitutes significant portion of a data center's total operational cost. Variations in parameters, such as power demand, electricity price, and renewable power generation effects the data center's power consumption cost. Therefore, in this paper, a smart power management system based on a robust energy cost optimization algorithm is proposed for the data center. The designed robust optimization algorithm coordinates data center workload, battery bank, diesel generators, renewable power, and trade electricity price in real-time and day-ahead power market to reduce expected energy consumption cost. The uncertain parameters, such as data center workload and renewable power are computed using forecasting algorithms. The optimization algorithm is used to limit unbalance power purchase from real-time power market due to uncertainty of real-time electricity price. The algorithm formulation is obtained using mixed-integer linear programming. Moreover, a model to calculate prices to be used in service level agreements with data centers' clients for on-demand cloud services is designed considering operational cost of the data center. Simulations are performed on actual data center workload, weather parameters, and electricity price. The results have shown that the proposed methodology is an effective tool to minimize data center operational cost.

Fundamental pricing laws and long memory effects in the day-ahead power market

We apply linear and nonlinear panel models to investigate fundamental pricing laws in the Greek day-ahead electricity market. We link the persistence behaviour and other dynamic properties of electricity price trajectories to an extended array of fundamental variables reflecting market conditions and the price course of energy commodities in global markets. Assuming that the dynamics of the intraday power pricing curve is adequately specified within a panel modelling framework with long memory and common unobserved stochastic effects, we are able to provide consistent estimates of price responsiveness to the main fundamental drivers and thus avoid biases induced by other popular modelling approaches. We also device a panel variant of the standard smooth-transition regression model to investigate regime switching effects in the price responsiveness mechanism. Empirical results suggest that Greek day-ahead electricity market prices respond significantly to hour-specific (load, marginal generation capacity) and global (Brent price, emission allowance rights cost) fundamental variables. In some intraday power delivery zones, these covariates are in a fractional cointegrating relationship with electricity prices being responsible for their drifting behaviour. The strength and type of cointegration changes across intraday trading periods in response to market conditions. The results of the nonlinear model demonstrate the ineffectiveness of linear model designs (either univariate or panel) to accurately estimate the levels of price risk and associate it with changes in the market climate.

Fundamental Pricing Laws and Long Memory Effects in the Day-Ahead Power Market

We apply linear and nonlinear panel models to investigate fundamentals pricing relationships in the Greek day-ahead power market. We link the persistence behaviour and other dynamic properties of power price trajectories to an extended array of fundamental variables reflecting market conditions and the price course of energy commodities in global markets. Assuming that the dynamics of the intraday power pricing curve is adequately specified within a panel modelling framework with long memory and common unobserved stochastic effects, we are able to provide consistent estimates of price responsiveness to the main fundamental drivers and thus avoid biases induced by other popular modelling approaches. We also device a panel variant of the standard smooth-transition regression model to investigate regime switching effects in the price responsiveness mechanism. Empirical results support that Greek day-ahead prices are in a fractional cointegrating relationship with hour-specific covariates (load, marginal generation capacity) as well as price determinants affecting the entire cross-section of hourly contracts (Brent price, emission allowance rights cost). The strength and type of cointegration often changes in a nonlinear way with the hour of power delivery or in response to market conditions. Our study has important implications for price risk management. The results of the nonlinear model demonstrate the ineffectiveness of linear models (either univariate or in a panel context) to accurately estimate the levels of price risk and associate it with changes in market conditions.

Data-Driven Bidding Strategy for DER Aggregator Based on Gated Recurrent Unit–Enhanced Learning Particle Swarm Optimization

Distributed energy resources (DERs) such as wind turbines (WTs), photovoltaics (PVs), energy storage systems (ESSs), local loads, and demand response (DR) are highly valued for environmental protection. However, their volatility poses several risks to the DER aggregator while formulating a profitable strategy for bidding in the day-ahead power market. This study proposes a data-driven bidding strategy framework for a DER aggregator confronted with various uncertainties. First, a data-driven forecasting model involving gated recurrent unit–enhanced learning particle swarm optimization (GRU-ELPSO) with improved mutual information (IMI) is employed to model renewables and local loads. It is critical for a DER aggregator to accurately estimate these components before bidding in the day-ahead power market. This aids in reducing the penalty costs of forecasting errors. Second, an optimal bidding strategy that is based on the information gap decision theory (IGDT) is formulated to address market price uncertainty. The DER aggregator is assumed to be risk-averse (RA) or risk-seeker (RS), and the corresponding bidding strategies are formulated according to the risk preferences thereof. Then, an hourly bidding profile is created for the DER aggregator to bid successfully in the day-ahead power market. The proposed data-driven bidding framework is evaluated using an illustrative system wherein a dataset is obtained from the PJM market. The results reveal the effectiveness of handling uncertainty by providing accurate forecasting results. In addition, the DER aggregator can bid effectively in the day-ahead power market according to its preference for robustness or high profit, with a suitable bidding profile.

Probabilistic model for nondispatchable power resource integration with microgrid and participation in the power market

Participation of nondispatchable renewable energy resources (RER) such as wind turbines (WT) and photovoltaic (PV) systems is one of the main challenges of these clean energy resources. Due to the uncertain nature of these resources, predictions of their amount of power in the day-ahead market is very difficult and associated with error. This paper introduces a novel model for the participation of the mentioned resource in the day-ahead power market based on error and economic loss minimization. An intra-market (IM) model was considered to update the predicted power of these resources before the planned time horizon. Furthermore, a new probabilistic forecasting model was proposed for uncertain parameters in both the day-ahead and intramarket. The mentioned model included an integrated system of renewable energy with another distributed generation, storage device, and demand response to compensate for all resource and load economic losses. By this approach, it can be claimed that, all microgrid participants i.e., load, producer and storage, have more profits in comparison with the power supply individually. To demonstrate the efficiency of the proposed model, we considered a test case that consisted of a wind turbine, PV, fuel cell, storage unit, and demand response, by taking into account the day-ahead, intra and unbalanced market. In this case study, daily, weekly, and monthly analyses were performed. The simulation results revealed the effectiveness and performance of the proposed modelling in comparison with other participation model.

Locational marginal price forecasting in a day-ahead power market using spatiotemporal deep learning network

Abstract Locational marginal pricing (LMP) in a deregulated electricity market is a result of bidding competition from nodes (locations). This paper presents a deep learning neural network for the 24 h-ahead LMP forecasting of a specific node in a zone at the PJM power market using other zonal prices. The presented method uses a Convolutional Neural Network (CNN), which is a well-known architecture for deep learning that has been applied successfully for image classification and pattern recognition. The CNN is used herein to extract features of real-time electricity prices by convolution, kernel, and pooling operations using spatiotemporal electricity prices in various zones and locations. The optimal parameters of the model were determined using a step-by-step manual approach to minimize the burden of brute force trial and error methods. Datasets from the PJM power market are used in simulations in which the proposed method is applied using Tensorflow and Keras Library. Various approaches are compared, and the results thus obtained demonstrate that the proposed method has higher accuracy than traditional methods in 24 h-ahead LMP forecasting.

Real-Time Estimation of the Short-Run Impact of COVID-19 on Economic Activity Using Electricity Market Data

In response to the COVID-19 emergency, many countries have introduced a series of social-distancing measures including lockdowns and businesses’ shutdowns, in an attempt to curb the spread of the infection. Accordingly, the pandemic has been generating unprecedented disruption on practically every aspect of society. This paper demonstrates that high-frequency electricity market data can be used to estimate the causal, short-run impacts of COVID-19 on the economy, providing information that is essential for shaping future lockdown policy. Unlike official statistics, which are published with a delay of a few months, our approach permits almost real-time monitoring of the economic impact of the containment policies and the financial stimuli introduced to address the crisis. We illustrate our methodology using daily data for the Italian day-ahead power market. We estimate that the 3 weeks of most severe lockdown reduced the corresponding Italian Gross Domestic Product (GDP) by roughly 30%. Such negative impacts are now progressively declining but, at the end of June 2020, GDP is still about 8.5% lower than it would have been without the outbreak.

Electricity trading based on distribution locational marginal price

This paper presents a novel day-ahead power market for distribution systems. Based on the linearized AC power flow model, the distribution locational marginal price for coupled active and reactive power can be calculated and decomposed into five components, i.e. (1) energy price; (2) loss price caused by nodal active power; (3) loss price caused by nodal reactive power; (4) congestion price and (5) voltage support price, which can provide price signals for distributed generator and aggregator in a distribution system to respond. The energy hub at different nodes can trade with each other and optimize their profit based on distribution locational marginal prices. Game theory is applied to solve the energy trading payment problem. The energy trading problem is decomposed into two subproblems, i.e. operation cost minimization problem and trading payment bargaining problem. The effectiveness and validity of the proposed method are illustrated with a modified IEEE 33-bus system.

A day-ahead power spot market clearing model considering incentive-based demand response

It is a gradual evolution path and the most common practice in the initial stage of power spot market construction in China to start with bidding without price on the demand side, which lead to a question of how to solve the demand respond (DR) in day-ahead power spot market. To this end, a day-ahead power market clearing model considering incentive-based demand response (IBDR) is proposed. The IBDR is mainly aimed at large users with certain load elasticity, which can adjust their production schedules and change their electricity load curve to obtain certain cost incentives. This clearing method can optimize the spot market clearing result to maximize the social benefit by coordinating the electricity purchase cost and DR incentive cost. In this process, large users can obtain certain economic compensation by providing DR and help the dispatching mechanism to solve the operation problems such as power grid congestion, which could improve the operation efficiency of thermal power units. Finally, a case study based on the IEEE-30 buses system is used to verify the effectiveness of the proposed method.

Source-load Dispatch Model for Electricity Day-ahead Market with Wind Power Penetration

Due to the intermittent and volatility of wind power output, the traditional power grid’s operation and dispatching mode and the adjustment capability of conventional units were unable to meet the demands of large-scale wind power penetration. Based on the characteristics of the day-ahead dispatching link in the spot power market, this paper combines the auxiliary service with the day-ahead power market transaction to form the day-ahead trading plan. At the same time, considering the conventional thermal power peak shifting and interruptible load peak shifting, a new market peak shifting optimization model reflecting the will of the source-load role is formed. On this basis, electricity day-ahead market dispatching model considering source-load and peak shifting is established, aiming at the maximum expected income of wind power plant. Through practical examples and scenario analysis, it is verified that the source-load peak shifting scheduling model will effectively improve the expected income and absorption rate of wind power plant.

Chance-constrained stochastic congestion management of power systems considering uncertainty of wind power and demand side response

This study proposes a new stochastic model based on chance constraints for network congestion management in the day-ahead power market. By jointly considering the uncertainty of wind power and demand side response, the proposed optimization model can help determine the optimal daily dispatch of generators and demand responsive loads to minimize the risk of transmission congestion. To this end, transmission line congestion probability (TLCP) is constructed in chance constraints to ensure system congestion less than a certain level. The indexes of load loss probability (LLP) and wind curtailment probability (WCP) are proposed and incorporated as chance constraints to achieve a high reliability of power supply and high utilization of wind generation, respectively. In the optimization process, the proposed stochastic optimization model is transformed to an equivalent deterministic model by using the probability distribution of random variables, and the influence of transmission system loss on transmission congestion management is considered. To calculate the satisfaction degree of chance constraints under the determined dispatch of generators and demand responsive loads, the probabilistic power flow based on the cumulant method is used, and the risk of transmission congestion level is quantified by the index, congestion risk value (CRV). Simulation results of a modified PJM 5-bus system and an IEEE 118-bus test system demonstrate the effectiveness of the proposed approach for congestion management in the day-ahead power market.