Day-ahead Electricity Market Research Articles

Joint market bid of a hydroelectric system and wind parks

This paper presents the model for finding an optimal joint bid of hydroelectric systems and wind parks on a day-ahead electricity market. The optimization problem is presented as a two-stage model where the goal of the first stage is to maximize the profit according to the main scenario and the obtained production plan is then tested in all other scenarios that can occur in the second stage where the goal is to minimize the profit reduction caused by imbalance penalties or changes in expected day-ahead prices. The optimization procedure is presented as a mixed integer linear optimization problem. The proposed model is applied in a case study in order to present the main model features. Production assembly in the case study is based on real power plants and consists of two cascade hydropower plants and two wind parks.

Robust bidding strategies and scheduling of a price‐maker microgrid aggregator participating in a pool‐based electricity market

This study presents a model for the activities of the price-maker microgrid aggregator (MGA). In this model, an MGA is considered to aggregate several microgrids (MGs) and be in charge of obtaining an optimal bidding strategy for MGs as well as scheduling their resources and demand. Two price-maker strategies (the marginal and non-marginal strategies for players) are proposed and the robust scheduling and optimal transactions of a price-taker MGA are also obtained in order to analyse different bidding behaviour of MGA. A robust optimisation is used in this model in order to capture uncertainties associated with renewable generation in the worst-case situation. Accordingly, the robust solution is obtained for the optimal scheduling of an MGA participating in the pool-based day-ahead electricity market. The proposed robust bidding strategies and scheduling of a price-maker MGA are obtained considering a hypothetical test system and the results are compared with the bidding strategy and robust scheduling of a price-taker MGA. The results show that the robust scheduling and also the market prices are completely changed for different strategies of the MGA. Also, using the proposed model for the price-maker MGA increases the profits of MGs.

Impact of Bilateral Contracts on Wholesale Electricity Markets: In a Case Where a Market Participant Has Dominant Position

This paper aims at tackling how the bilateral contracts affect wholesale electricity markets. It examines different levels of bilateral contracts among producers and demand aggregators, aiming to quantify their effect. In addition, it focuses on markets where bilateral contracts could be used as a tool by market participants with a dominant position. Further, the paper examined a case with asymmetrical portfolios, namely where a market participant has a dominant position as in case of Greece, aiming to investigate if bilateral contracts can be used as a tool to manipulate the market. The simulations have been done by an optimization model that provides the economic dispatch and clearing of the day-ahead electricity market. The model incorporated bilateral contracts with committed generating capacity from producers, as well as dynamic bidding strategy per market participant. Results provide useful insights on the design of electricity markets, especially in case of designing voluntary energy exchanges where a market participant has a dominant position.

Robust trading strategies for a waste-to-energy combined heat and power plant in a day-ahead electricity market

Waste-to-energy (WtE) technologies have been used all over the world as they can solve the dilemma of waste management, energy demand, and global warming. Many modern WtE plants are built and operated in a combined heat and power (CHP) mode due to the high overall energy efficiency. This paper studies robust trading strategies for a WtE CHP plant which sells electricity in a day-ahead electricity market and exports heat to a district heating network. Owing to the requirements of the day-ahead electricity market, plant operators must determine the trading strategy one day before real delivery of electricity. However, many key problem parameters including electricity price, heat demand, and the amount of waste delivered to the plant are uncertain at the day-ahead stage. To derive robust electricity trading strategies for the WtE CHP plant under different types of uncertainty, a two-stage robust optimization model is developed and a solution procedure based on the column-and-constraint generation method is designed. A case study is also performed to illustrate the effectiveness of the robust model and the solution procedure.

Developing Bidding and Offering Curves of a Price-Maker Energy Storage Facility Based on Robust Optimization

This paper presents an algorithm to construct hourly bidding and offering curves to purchase and sell electricity for a price-maker merchant energy storage facility participating in a day-ahead electricity market. Hourly generation and demand price quota curves (GPQCs and DPQCs) are used to model the price impact of storage operation in the participation strategy problem of a price-maker storage facility in the market. This paper introduces a max-min mixed-integer linear programming model to present a participation strategy to manage the risk of uncertainty associated with forecasted GPQCs and DPQCs by robust optimization. The max-min formulation is converted to its equivalent linear maximization formulation based on the worst case scenario during charging and discharging hours. Then, an algorithm is proposed to build hourly bidding and offering curves. To do so, the confidence intervals for GPQCs and DPQCs are divided into subintervals and the robust mixed-integer linear formulation is solved sequentially for each subinterval of GPQCs and DPQCs. Sequential constraints are applied to ensure the decreasing and increasing nature of the bidding and offering curves, respectively. Then, hourly bidding and offering curves are built based on the obtained scheduling and corresponding price results. The proposed bidding and offering strategy is presented and validated using numerical simulations.

CVaR-based stochastic wind-thermal generation coordination for Turkish electricity market

Uncertainties in wind power forecast, day-ahead and imbalance prices for the next day possess a great deal of risk for the profit of generation companies participating in a day-ahead electricity market. Generation companies are exposed to imbalance penalties in the balancing market for unordered mismatches between associated day-ahead power schedule and real-time generation. Coordination of wind and thermal power plants alleviates the risks raised from wind uncertainties. This paper proposes a novel optimal coordination strategy by balancing wind power forecast deviations with thermal units in the Turkish day-ahead electricity market. The main focus of this study is to provide an optimal trade-off between the expected profit and the risk under wind uncertainty through conditional value at risk (CVaR) methodology. Coordination problem is formulated as a two-stage mixed-integer stochastic programming problem, where scenario-based wind power approach is used to handle the stochasticity of the wind power. Dynamic programming approach is utilized to attain the commitment status of thermal units. Profitability of the coordination with different day-ahead bidding strategies and trade-off between expected profit and CVaR are examined with comparative scenario studies.

A power system scheduling model with carbon intensity and ramping capacity constraints

The integration of European electricity markets aims at market coupling among interconnected power systems and the evolution of environmentally friendly technologies. This process is anticipated to utilize more efficiently the flexible generation and interconnections transmission capacity and provide environmental and economic benefits to final consumers. This paper presents a mixed integer linear programming model for the optimal scheduling of a power system (unit commitment problem) simulating the day-ahead electricity market. The model determines the optimal daily power generation mix, the electricity trade with neighboring countries, the evolution of the system's marginal price and the resulting environmental impact. The model incorporates CO2 emissions intensity constraints and introduces flexible ramping products, in addition to reserve requirements, aiming to identify their impacts on both operational and economic decisions. The model is applied on the Greek power system and its interconnections with neighboring power systems in Southeast Europe. The proposed approach can provide useful insights on the optimal generation and interconnections portfolio that meets the real electricity market operating needs of contemporary power systems with environmental and ramping capacity constraints.

Deviation Cost Allocation of DISCOM Using Shapley Value

Distribution company (DISCOM) bids into day-ahead (DA) electricity market to serve the DA schedules of its loads put together along with distributed generation (DG) schedules. At real time (RT), the loads/DGs (participants) may deviate from their DA schedules and this deviation has to be settled at RT price. Any deviation by a participant on a feeder will change the total network losses. This leads to deviation penalty (cost) to DISCOM which should be allocated to the responsible participants. This deviation cost consists of two parts: first is due to deviation in DA power schedules and second is due to the change in network losses by these power deviations. In this article, the problem of allocating deviation cost is formulated as cooperative game and Shapley value is used to allocate this deviation cost. The deviation in schedules of DGs has also been modeled into game framework. The method has been tested on a system with different scenarios of power deviation of participants in RT. The results obtained are stable and consistent for both loads and DGs in deviation cost allocation.

Commercial Demand Response Programs in Bidding of a Technical Virtual Power Plant

Regarding the potentials of activating commercial consumers in demand response programs, this paper proposes a new framework for energy scheduling of an active distribution network based on the concept of technical virtual power plant (TVPP), considering operational constraints of distribution network. The TVPP enables presence of commercial buildings and other distributed energy resources in day-ahead (DA) electricity market, as a price maker agent. In this regard, a bilevel optimization framework is designed to optimize the bidding strategy of TVPP in the DA market with the main goal of maximizing TVPP profit. The upper-level problem maximizes the TVPP profit, while in the lower level, market is cleared from independent system operator viewpoint. Using Karush—Kuhn–Tucker optimality conditions and strong duality theory, the proposed bilevel problem is transformed into mixed integer linear programming problem. Implementing the model on the Roy Billinton Test System (RBTS) test system demonstrates the applicability of the proposed model.

Novel Approaches for the Clearing of the European Day-Ahead Electricity Market

In this paper, three novel approaches are presented for the clearing of the European day-ahead electricity market, incorporating the majority of the currently tradable products, namely the simple hourly orders, the block orders, the complex orders, and the “Prezzo Unico Nazionale” (PUN) orders. The first approach employs a Master Problem and a Sub-Problem sequentially solved within an iterative process, whereas two single-model approaches are also formulated for the efficient handling of the market orders’ clearing conditions and the handling of non-intuitive bilateral exchanges. In the single-model approaches, the clearing conditions of the block, complex, and PUN orders are explicitly incorporated in the Sub-Problem, by utilizing a Mixed Complementarity Problem formulation. The three approaches are compared in terms of solution efficiency (social welfare) and computational requirements. A sensitivity analysis is performed for all three approaches, in order to evaluate the efficiency of the attained solution with respect to the method used for the designation and subsequent elimination of all attained Paradoxically Accepted Orders.

Bivariate Probabilistic Wind Power and Real-Time Price Forecasting and Their Applications to Wind Power Bidding Strategy Development

We build an advanced offer curve in the day-ahead electricity market for wind power producers based on the multivariate (bivariate or two-dimensional) distribution of the real-time (RT) price and wind power forecasting errors. The bivariate distribution results in more profitable offers based on the conditional probability of wind power forecasting errors with respect to the RT price forecasting errors. The standard deviation of the bivariate distribution is reduced through a novel probabilistic wind power forecasting algorithm based on a parametric approach to further increase the profitability of the offer curve. In conclusion, the test of the advanced offer curve on the data sampled from the Iberian peninsula shows that the offer curve of the bivariate distribution has a higher profitability than offer curves of the marginal distribution.

Co-Optimization of Energy and Reserve Capacity Considering Renewable Energy Unit with Uncertainty

This paper proposes a system model for optimal dispatch of the energy and reserve capacity considering uncertain load demand and unsteady power generation. This implicates uncertainty in managing the power demand along with the consideration of utility, user and environmental objectives. The model takes into consideration a day-ahead electricity market that involves the varying power demand bids and generates a required amount of energy in addition with reserve capacity. The lost opportunity cost is also considered and incorporated within the context of expected load not served. Then, the effects of combined and separate dispatching the energy and reserve are investigated. The nonlinear cost curves have been addressed by optimizing the objective function using robust optimization technique. Finally, various cases in accordance with underlying parameters have been considered in order to conduct and evaluate numerical results. Simulation results show the effectiveness of proposed scheduling model in terms of reduced cost and system stability.

Decision making for sustainable aggregation of clean energy in day-ahead market: Uncertainty and risk

Abstract This paper addresses a strategy for decision-making of sustainable aggregation for clean energy participating in a day-ahead electricity market. The clean energy consists of wind turbines, photovoltaic arrays and energy storage, contributing to a better resilience of the system. The power delivered by a wind turbine or by a photovoltaic array is most certainly in deviation from the value associated with the bid accepted at a closing of a day-ahead electricity market. The deviation is due to the unpredicted variable nature of the respective sources of energy, leading to uncertainty and may imply a risk of loss of profit. So, uncertainty must be considered, and the addressed strategy considers uncertainty by scenarios of historical data and risk by the conditional value at risk. The strategy is a unified approach based on a risk-constrained and a two-stage stochastic optimization problem rewritten as a mixed-integer linear programming problem. A case study is presented to illustrate the main result and drive conclusions.

Battery Storage Systems in Smart Grid Optimised Buildings

The building sector is responsible for a significant proportion of the consumed energy and the consequent carbon emissions. Currently, electricity and natural gas are the most popular fuels used in the UK Services sector and the industry. Furthermore, buildings constitute a key component of the power network, in both its current conventional form and its evolution, the smart grid. The smart grid is expected to integrate energy storage, distributed generation and buildings into the network. This paper introduces the concept of Smart Grid Optimised Buildings (SGOBs), recognising the importance of energy storage to establish a dynamic interaction between the building and the smart grid. SGOBs are expected to be fully electric, make the best use of the available resources and utilise their embedded battery storage systems to respond to notifications issued by the smart grid and to dynamic electricity prices. Assuming that buildings have access to the day-ahead electricity market, initial results show that battery storage can be successfully used to change a building’s electricity profile and perform load-shifting (arbitrage) and peak-shaving while the excess electricity is exported back to grid to take advantage of the price difference and relieve pressure on the infrastructure.

Hybrid model using three-stage algorithm for simultaneous load and price forecasting

Short-term load and price forecasting is an important issue in the optimal operation of restructured electric utilities. This paper presents a new intelligent hybrid three-stage model for simultaneous load and price forecasting. The proposed algorithm uses wavelet and Kalman machines for the first stage load and price forecasting. Each of the load and price data is decomposed into different frequency components, and Kalman machine is used to forecast each frequency components of load and price data. Then a Kohonen Self Organizing Map (SOM) finds similar days of load frequency components and feeds them into the second stage forecasting machine. In addition, mutual information based feature selection is used to find the relevant price data and rank them based on their relevance. The second stage uses Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for forecasting of load and price frequency components, respectively. The third stage machine uses the second stage outputs and feeds them into its MLP-ANN and ANFIS machines to improve the load and price forecasting accuracy. The proposed three-stage algorithm is applied to Nordpool and mainland Spain power markets. The obtained results are compared with the recent load and price forecast algorithms, and showed that the three-stage algorithm presents a better performance for day-ahead electricity market load and price forecasting.

Put Option Pricing and Its Effects on Day-Ahead Electricity Markets

In this paper, the impacts of strike and premium prices of put option contracts on put option and day-ahead electricity markets are studied. To this end, first a comprehensive equilibrium model for a joint put option and day-ahead markets is presented. Interaction between put option and day-ahead markets, uncertainty in demand, and elasticity of consumers to strike price, premium price, and day-ahead price are taken into account in this model. Then, a formula for computing strike prices at which producers and consumers are willing to trade put option is presented and a new method for put option pricing is proposed. By applying the presented model to a test system, the interaction between the put option and day-ahead markets is studied.

Stochastic interval-based optimal offering model for residential energy management systems by household owners

This paper proposes an optimal bidding strategy for autonomous residential energy management systems. This strategy enables the system to manage its domestic energy production and consumption autonomously, and trade energy with the local market through a novel hybrid interval-stochastic optimization method. This work poses a residential energy management problem which consists of two stages: day-ahead and real-time. The uncertainty in electricity price and PV power generation is modeled by interval-based and stochastic scenarios in the day-ahead and real-time transactions between the smart home and local electricity market. Moreover, the implementation of a battery included to provide energy flexibility in the residential system. In this paper, the smart home acts as a price-taker agent in the local market, and it submits its optimal offering and bidding curves to the local market based on the uncertainties of the system. Finally, the performance of the proposed residential energy management system is evaluated according to the impacts of interval optimistic and flexibility coefficients, optimal bidding strategy, and uncertainty modeling. The evaluation has shown that the proposed optimal offering model is effective in making the home system robust and achieves optimal energy transaction. Thus, the results prove that the proposed optimal offering model for the domestic energy management system is more robust than its non-optimal offering model. Moreover, battery flexibility has a positive effect on the system’s total expected profit. With regarding to the bidding strategy, it is not able to impact the smart home’s behavior (as a consumer or producer) in the day-ahead local electricity market.

Baltic Power Systems’ Integration into the EU Market Coupling under Different Desynchronization Schemes: A Comparative Market Analysis

Currently, the power transmission system of the Baltic states is synchronized with the Integrated/Unified Power System (IPS/UPS), which includes the Russian grid, and the IPS/UPS provides frequency regulation and system security within the Baltic states. Since joining the European Union (EU) in 2004, the Baltic states have been following the EU’s energy policy targets. The Baltics are presently participating in a European electricity market, i.e., the NordPool market, while they are expected to join the pan-European electricity market—the European target model for power market integration. Moreover, from a power grid perspective, EU energy policies intend to desynchronize the power grid of the Baltic states from the IPS/UPS over the coming years. This paper evaluates these policy trends through market impacts, and it complements existing studies on Baltic-IPS/UPS desynchronization in terms of wholesale electricity prices, generation surpluses, primary reserve adequacy, and redispatch costs. Participation of the Baltic states in the integrated pan-European day-ahead electricity market with zonal pricing was modeled for 2030, followed by a national redispatch, with detailed power grid modeling of Baltic states to solve potential intrazonal congestion. The simulation results imply the superiority of the Baltics’ synchronization to continental Europe, compared to the other schemes.

Virtual storage plant offering strategy in the day-ahead electricity market

Energy storage is gaining an important role in modern power systems with high share of renewable energy sources. Specifically, large-scale battery storage units (BSUs) are an attractive solution due to their modularity, fast response and ongoing cost reduction.This paper aims to formulate, analyze and clarify the role of merchant-owned BSUs in the day-ahead electricity market. It defines virtual storage plant (VSP) as a set of BSUs distributed across the network. A VSP offering model is formulated as a bilevel program in which the upper-level problem represents the VSP profit maximization and operation, while the lower-level problem simulates market clearing and price formation. This mathematical problem with equilibrium constraints (MPEC) is converted into a mixed-integer linear program (MILP). This is afterwards expanded to a game of multiple VSPs formulating an equilibrium problem with equilibrium constraints (EPEC), which is solved using the diagonalization procedure.The proposed model is applied to an updated IEEE RTS-96 system. We evaluate the impact VSPs have on the locational marginal prices and compare the coordinated approach (all BSUs operated under a single VSP), i.e. the MPEC formulation, to the competitive approach (multiple VSPs competing for profit), i.e. the EPEC formulation.

A Multi-Stage Price Forecasting Model for Day-Ahead Electricity Markets

Forecasting hourly spot prices for real-time electricity markets is a key activity in economic and energy trading operations. This paper proposes a novel two-stage approach that uses a combination of Auto-Regressive Integrated Moving Average (ARIMA) with other forecasting models to improve residual errors in predicting the hourly spot prices. In Stage-1, the day-ahead price is forecasted using ARIMA and then the resulting residuals are fed to another forecasting method in Stage-2. This approach was successfully tested using datasets from the Iberian electricity market with duration periods ranging from one-week to ninety days for variables such as price, load and temperature. A comprehensive set of 17 variables were included in the proposed model to predict the day-ahead electricity price. The Mean Absolute Percentage Error (MAPE) results indicate that ARIMA-GLM combination performs better for longer duration periods, while ARIMA-SVM combination performs better for shorter duration periods.