Day-ahead Electricity Research Articles

On the value and potential of demand response in the smart island archipelago

Existing studies propose different demand response models and often test them on islands that represent test-beds for new technologies. However, proposed models are often simplified and integrated into energy system models that do not consider the existing limitations of the power grid. This study proposes a novel demand response model based on price differentials on the day-ahead electricity market. The model is implemented in the distribution system that considers all relevant grid constraints. The case study is conducted in an archipelago characterised by a medium-voltage distribution system connected to the mainland grid. The obtained results showed that the implementation of the proposed demand response model caused a 0.13 kV voltage deviation which did not cause voltage issues for the observed distribution system. The breakpoint incentive was achieved for an incentive value of 23% of the day-ahead market, and the demand response was not activated for higher values than the breakpoint incentive. The highest savings amounted to 258.7 € for the scenario with the highest flexibility allowed. The results implicate that implementing the demand response model in the grid would benefit all observed stakeholders in the system.

Demand Response: Smart Market Designs for Smart Consumers

We study Peak-Time-Rebates (PTR) contracts in day-ahead electricity markets.Such contracts reward customers for reducing their consumption when wholesale prices are high. We start by pointing out that these market designs create arbitrage opportunities which, under asymmetric information, incentivize strategic consumers to inflate their baseline. We then show that an incentive compatible PTR design is equivalent to a variable Critical-Peak-Pricing design (vCPP), in which customers have to purchase their peak consumption at the spot price. Under asymmetric information, a relevant question is thus to design vCPP contracts optimally in order to achieve high enrollment rates under voluntary opt-in. This problem has different solutions depending on whether policy-makers choose to maintain existing cross-subsidies or not.

Forecast of Area Prices in Japanese Day-Ahead Electricity Market using Open Data

Forecast of Area Prices in Japanese Day-Ahead Electricity Market using Open Data

Optimal two-level active and reactive energy management of residential appliances in smart homes

The optimal performance of the smart home energy management system (SHEMS) in coordinating and integrating the equipment in the house plays a significant role in increasing the efficiency and economic benefits of the smart home. This paper proposes a two-level optimization algorithm for the energy management of residential appliances within a smart home, including interruptible, uninterruptible, thermostatically controlled, and non-schedulable loads, as well as the charging/discharging strategies of electric vehicles (EVs) and energy storage systems (ESSs) in the presence of distributed energy resources (DERs). In the first level of optimization, to minimize the cost of electricity, the operating time of smart home components is determined by SHEMS, and active power is exchanged between equipment. At this level, the impact of demand response (DR) constraints on the cost and load factor (LF) of the smart home have also been investigated. In the second level, considering the optimal cost obtained in the first level, in addition to the active power, the reactive power required by loads is also provided by using the additional capacity of EV and ESS inverters and improves the power factor (PF) of the house at the connection point to the grid. Day-ahead electricity costs are calculated depending on different pricing signals (TOU, RTP) with the power exchange capacity (buying/ selling) with the grid. The effectiveness of the proposed strategy is examined on a typical smart home over a 24-h. By reviewing the numerical results obtained for the cost and the LF in different scenarios, and observing the increase of the PF of home from 0.65 to 0.94 and also examining the performance of the equipment during the planning time, the efficiency of the model is proved.

Modeling and Estimating Volatility of Day-Ahead Electricity Prices

We model day-ahead electricity prices of the UK power market using skew generalized error distribution. This distribution allows us to take into account the features of asymmetry, heavy tails, and a peak higher than in normal or Student’s t distributions. The adequacy of the estimated volatility model is verified using various tests and criteria. A correctly specified volatility model can be used for analyzing the impact of reforms or other events. We find that, after the start of the COVID-19 pandemic, price level and volatility increased.

Optimal stochastic scheduling of reconfigurable active distribution networks hosting hybrid renewable energy systems

Renewable energy sources (RESs) have a remarkable role in advancing the goals of restructured power systems to reduce greenhouse gas emissions and increase the level of reliability. However, due to the non-uniform utilisation of these resources in various sectors of power grids, a major part of the generated renewable energies is spilled to satisfy the power system constraints. Motivated by this challenge, the role of the reconfiguration mechanism in maximising the utilisation of RESs in active distribution networks (ADNs) is investigated herein. To this end, a two-stage stochastic model is presented for optimal scheduling of reconfigurable distribution networks in the presence of high-power hybrid wind/photovoltaic systems. The main goal of the presented model is to maximise the hybrid system owner's profit. In the first stage of the presented structure, the optimal hourly bilateral dispatches between the hybrid system and the ADN in the day-ahead electricity market are determined to maximise the hybrid system owner's profit. In the second stage, the power spillage of the hybrid renewable energy systems are minimised using reconfiguration technology in the real-time electricity market. For practical implementation, the proposed operational strategy is applied to the modified 33-bus and 69-bus distribution test systems, and is solved using GAMS software. The simulation results indicate that the proposed strategy can considerably reduce renewable power spillage, increase the hybrid system owner's profit, and decrease total active power loss of the ADN. According to the obtained results in the 33-bus test system, the profit of the hybrid system owner is increased by up to 6.8% as well as the total active power loss being decreased by up 75.58% through the presented structure.

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.

Deep sequence to sequence Bi-LSTM neural networks for day-ahead peak load forecasting

The power industry is currently facing the problem of an electricity supply–demand imbalance. The most inexpensive and efficient solution to alleviate this imbalance is to decrease electricity demand. Local electrical utilities should deploy demand response programs (DRP), and short-term peak demand forecasting (STPDF) plays a crucial role in their successful deployment. In residential sectors, peak demand forecasting is also critical because the energy policies, technological growth, and changing climate are further increasing the peak demand. Therefore, an accurate peak demand forecasting will help utility companies in avoiding blackouts and secure a continuous power supply by implementing subsidy schemes such as DRP. However, daily peak load is volatile, nonstationary, and nonlinear in nature, and hence it is hard to predict it accurately. This research work for the first time has attempted to design, implement, and test deep bidirectional long short-term memory based sequence to sequence (Bi-LSTM S2S) regression approach for “day-ahead” peak demand forecasting and has accomplished preliminary success. The day-ahead peak electricity demand forecasting model is designed and tested using the MATLAB software. For performance comparison, shallow Bi-LSTM S2S, shallow LSTM S2S, deep LSTM S2S, Levenberg-Marquardt backpropagation artificial neural networks (LMBP-ANN), and medium Gaussian support vector regression (MG-SVR) forecasting models are also developed and tested. Mean absolute percentage error (MAPE) and Root Mean Squared Error (RMSE) are used as performance metrics. It has been found out that in terms of both performance metrics, the proposed deep Bi-LSTM S2S day-ahead “peak” forecasting model has outperformed all the other models on both public holidays and normal days. The load pattern on public holidays is always different than on normal days, and there is always less data available in contrast to the normal days. Therefore, it is hard to accurately forecast their load.

A Prediction Market Trading Strategy to Hedge Financial Risks of Wind Power Producers in Electricity Markets

Wind power producers participating in day-ahead electricity markets are compelled to pay imbalance costs if they do not generate the same amount of power as they had bid for. These imbalance costs comprise a significant proportion of their income. To reduce the risk of such financial losses, this paper employs the idea of trading in a separate prediction market, as a hedging method. In prediction markets, participants trade shares associated with a certain outcome of an event. We propose that the wind power producers might participate in a prediction market to trade the future value of the wind power and by taking an opposite position in comparison to the electricity market, the imbalance costs will be offset through payouts in the prediction market. Wind power is modelled as a stochastic variable and an optimal trading strategy is developed where the trading volume in the prediction market is analytically derived and formulated by minimising the maximum possible loss and pricing of shares is determined via indifference utility condition. The results suggest that the proposed method limits the loss values and improves the risk measures.

Spot trading strategy considering multiple constraints for electricity retailers

In order to solve the inert trading and irrational trading of electricity retailers in the spot market trading environment, increase the income of electricity retailers and reduce transaction risk, a spot trading strategy considering multiple constraints for electricity retailers is proposed. Similar days are selected by analyzing the date type and the temperature difference of the spot market trading day, on the basis of similar days, the electricity consumption information of electricity retailers’ agent users, day-ahead and real-time electricity price of sport trading market are predicted, combing the user-side excess profit recovery fee, mid-to-long term shortage recovery fee and mid-to-long term curve deviation recovery fee restrictions in Shanxi Province spot trading rules, optimize the mid-to-long term daily 96 points daily electricity purchase curve and day-ahead 96 points daily electricity biding curve, and use the real data of Shanxi spot market transactions to calculate the multi-day income of the electricity retailers to verify the practicability of the spot trading strategies.

Impact of domestic PV systems in the day-ahead Iberian electricity market

The integration of residential photovoltaic (PV) systems for self-consumption is expected to unlock a variety of economic, technical and social benefits, such as the creation of local work and the reduction of the price of electricity, CO2 emissions and electrical power losses in the transmission network. In spite of all these advantages, regulatory barriers prevented the development of renewable self-consumption in Spain prior to October 2018, when a new regulation introduced the right to self-consume electrical energy without charges. This work aims to estimate the effect of residential self-consumption on the Iberian electricity market. To achieve this goal, a ceteris paribus approach was adopted, the accumulated scattered PV self-production was turned into a reduction of the aggregate demand in the market and the new clearing point was established. Based on 2017 market data, the results showed that a 1% curtailment of the annual demand due to PV self-consumption could reduce the cost of the market-traded energy by almost 2%.

Optimization model of WPO-PVO-ESO cooperative participation in day-ahead electricity market transactions considering uncertainty and CVaR theory

Affected by uncertainty, clean energy power generators need reserve units to alleviate the problems caused by its uncertainty. With the development of energy storage technology, the characteristics of flexibly charging and discharging can reduce the deviations generated by clean energy power generators participating in the electricity market. Firstly, this paper constructs mechanisms for wind power operators, photovoltaic power operators, and energy storage operators in independent and cooperation modes. Secondly, uncertainty analysis models for wind power and photovoltaic power are built, and a solution for processing the uncertainty of clean energy power generation is proposed based on the Latin hypercube scenario generation and synchronous back generation reduction method. Thirdly, day-ahead trading optimization model for wind power, photovoltaic power, and energy storage operators in independent mode and cooperative mode are constructed under risk-neutral situation and CVaR-based risk non-neutral situation, respectively. Finally, a northern area in China is selected as research background to set multiple cases, and CPLEX solver is applied to solve the optimization model, thus verifying the proposed strategy and model’s effectiveness.

A Multiagent Competitive Bidding Strategy in a Pool-Based Electricity Market With Price-Maker Participants of WPPs and EV Aggregators

Large-scale renewable energy suppliers and electric vehicles (EVs) are expected to become dominated participants in future electricity market. In this article, a competitive bidding strategy is formulated for wind power plants (WPPs) and EV aggregators in a pool-based day-ahead electricity market. A bilevel multiagent based model is proposed to study their bidding behaviors, with market clearing completion in the lower level and revenue maximization in the upper level. A stochastic framework is developed to incorporate the uncertainties in maximal power production of WPPs and EV aggregators and bid prices of other participants. The process of bidding decision is formulated as a stochastic game with incomplete information, in which electricity suppliers including WPPs and EV aggregators are considered as players of the game, their lack of information in this stochastic market environment is counterbalanced by a multiagent reinforcement learning algorithm named win or learn fast policy hill climbing (WoLF-PHC) with maximizing their own profits by self-game. The feasibility and effectiveness of the proposed model and the WoLF-PHC solution approach are successfully illustrated using a modified IEEE 6-bus system and a modified 118-bus system with different numbers of market players.

ACCESSING THE EFFECT OF RENEWABLES ON THE WHOLESALE POWER MARKET

The purpose of this paper is to examine how rising wind energy generation (in MWh) impact the wholesale power market's volatility (in SEK) across four bidding regions in Sweden. Prior investigations show that though the increase in electricity production from wind energy lowers the average day-ahead electricity wholesale prices, however, uncertainty and volatility of market price could rise due to wind energy's intermittent nature. This study results show that Swedish power market experiences higher price volatility in long-run frequency when the generation of wind electricity increases. The reason for this high and volatile electricity price might be found from inflexible baseload power generation. The paper further suggests that volatility in the Swedish power market could be increased due to ambitious renewable electricity target by the Swedish government. The analysis concludes by providing the evidence that further adjustment in regard to the energy and regulatory policies might foster the better integration of a higher share of renewables into the power system.Keywords: Renewable Electricity, Wind Energy, Electricity Market, Price Volatility, Regulatory PoliciesJEL Classification: Q470DOI: https://doi.org/10.32479/ijeep.10756

Development of Optimal Day-Ahead Electricity Pricing Scheme using Real Coded Genetic Algorithm under Demand Response Environment

Real-time pricing in a Smart Grid scenario allows consumers to move their time-insensitive loads to off-peak hours and maintains the power balance between the demand side and supply side. This scheme has a strong impact on customer behaviour, network operations, and overall control of the power grids. In this proposed work, Real coded Genetic Algorithm (RGA) is used to develop a Day Ahead Real-Time electricity-Pricing (DARTP) model. The established scheme maximizes the benefit of the energy provider without reducing the minimum daily consumption rate, the consumer response to the reported electricity prices, and the constraints of distribution networks. The RGA results demonstrate that the calculated optimal prices bring higher benefits for consumers and energy providers than the posting of market prices directly to consumers on the day ahead. The proposed setup is tested with a 32-node distribution bus system. Simulation results reveal that the deployed methodology will help the participants to shift the peak load time to base load time by receiving optimal DARTP, thereby reducing excessive consumption made during the instance of peak load. The obtained DARTP will be sent to the consumers through the deployment of an Advanced Metering System.

Optimising block bids of district heating operators to the day-ahead electricity market using stochastic programming

The wide spread of district heating in Denmark offers a massive potential for flexibility in an energy system with intermittent renewable energy production. To leverage this potential, a cost-efficient power market integration of combined heat and power (CHP) units in district heating systems is important. We propose a stochastic program optimising block bids to the day-ahead market for CHP units in district heating systems under uncertain power prices. Block bids allow the internalisation of start-up costs. Based on the stochastic program, we develop a solution approach based on sample average approximation (SAA) to solve the stochastic program for a large number of price scenarios. We present results for a case study from Middelfart, Denmark. The system consists of two sub-networks that have lately been connected. We analyse the block bidding behaviour with and without connection using real data from different seasons. The results show that the bidding varies significantly depending on seasons and the layout of the network. Furthermore, the results show that the solution approach based on SAA reduces computation time significantly while maintaining solution quality.

Equilibrium Model of Electricity Market Bidding Considering Incentive-based Demand Response

In the initial stage of electricity market construction, incentive-based demand response can effectively mitigate price spikes. Considering the overall equilibria of the market, it is of great significance to study the impact of incentive-based demand response on market clearing and participants’ behaviours. Firstly, this paper analyzes the compensation methods of incentive- based demand response and establishes an equilibrium model of day-ahead electricity market bidding that takes into account demand response. Secondly, considering the overall equilibrium conditions of the market, the overall equilibrium model of the market is established. Results show that the incentive-based demand response mechanism has a restraining effect on the generators’ bidding, and the load reduction effect is prominent when the network is congested.

Strategic bidding via the interplay of minimum income condition orders in day-ahead power exchanges

In this paper we study the so-called minimum income condition order, which is used in some day-ahead electricity power exchanges to represent the production-related costs of generating units. This order belongs to the family of complex orders, which imply non-convexities in the market clearing problem. We demonstrate via simple numerical examples that if more of such bids are present in the market, their interplay may open the possibility of strategic bidding. More precisely, we show that by the manipulation of bid parameters, a strategic player may increase its own profit and potentially induce the deactivation of an other minimum income condition order, which would be accepted under truthful bidding. Furthermore, we show that if we modify the objective function used in the market clearing according to principles suggested in the literature, it is possible to prevent the possibility of such strategic bidding, but the modification raises other issues.

Electricity pricing using a periodic GARCH model with conditional skewness and kurtosis components

Abstract This paper extends the investigation of the stochastic properties of electricity price growth rates beyond their first two conditional moments allowing for the impact of seasonality on their parameters. The main contributions include the breakdown of electricity price risk into its pure and skewness price components and the development of a risk neutral forecasting equation for electricity prices. Empirical results using ten-years of hourly wholesale prices from the Day-Ahead electricity market in Germany depict the presence of seasonality, strong mean reversion and up-to third degree time-varying moments.

Day-Ahead Wind Power Forecasting Based on Wind Load Data Using Hybrid Optimization Algorithm

Accurate wind power forecasting is essential to reduce the negative impact of wind power on the operation of the grid and the operation cost of the power system. Day-ahead wind power forecasting plays an important role in the day-ahead electricity spot trading market. However, the instability of the wind power series makes the forecast difficult. To improve forecast accuracy, a hybrid optimization algorithm is established in this study, which combines variational mode decomposition (VMD), maximum relevance & minimum redundancy algorithm (mRMR), long short-term memory neural network (LSTM), and firefly algorithm (FA) together. Firstly, the original historical wind power sequence is decomposed into several characteristic model functions with VMD. Then, mRMR is applied to obtain the best feature set by analyzing the correlation between each component. Finally, the FA is used to optimize the various parameters LSTM. Adding the forecasting results of all sub-sequences acquires the forecasting result. It turns out that the proposed hybrid algorithm is superior to the other six comparison algorithms. At the same time, an additional case is provided to further verify the adaptability and stability of the proposed hybrid model.