Day-ahead Stage Research Articles

Energy Flexibility Management Based on Predictive Dispatch Model of Domestic Energy Management System

This paper proposes a predictive dispatch model to manage energy flexibility in the domestic energy system. Electric Vehicles (EV), batteries and shiftable loads are devices that provide energy flexibility in the proposed system. The proposed energy management problem consists of two stages: day-ahead and real time. A hybrid method is defined for the first time in this paper to model the uncertainty of the PV power generation based on its power prediction. In the day-ahead stage, the uncertainty is modeled by interval bands. On the other hand, the uncertainty of PV power generation is modeled through a stochastic scenario-based method in the real-time stage. The performance of the proposed hybrid Interval-Stochastic (InterStoch) method is compared with the Modified Stochastic Predicted Band (MSPB) method. Moreover, the impacts of energy flexibility and the demand response program on the expected profit and transacted electrical energy of the system are assessed in the case study presented in this paper.

Optimal scheduling of energy storage under forecast uncertainties

Recent studies have concluded that battery energy storage will soon be economically competitive if its cost continues to decline. The authors propose a two-stage look-ahead daily scheduling strategy for distributed energy storage located in distribution networks with a substantial photovoltaic (PV) penetration. They assume that the load serving entity operates this energy storage to harness simultaneously multiple streams of benefits: energy arbitrage, peak shaving, minimising deviations from the load forecast and regulation service. To determine the optimal capacity bid into the day-ahead regulation market and address the price, load, and solar forecast uncertainties, they propose a two-stage optimisation model that bids regulation capacity on the day ahead and determines the storage dispatch schedule in real time. At the day-ahead stage, the load serving entity reserves a portion of the storage capacity for regulation, while the remaining capacity is dispatched for energy arbitrage, peak shaving and minimising the deviations from the forecast. Result suggests that regulation services account for the majority of these benefits. The energy storage is dispatched for peak shaving and forecast-deviation minimisation from around noon to late evening. During the rest of day, storage is dispatched primarily for regulation services.

Interactions between the design of short-term electricity markets in the CWE region and power system flexibility

Short-term electricity markets are generally defined as markets that take place from the day-ahead stage until physical generation and consumption. These markets include day-ahead, intra-day, and real-time balancing markets. In Europe, the first two are managed by power exchanges, while the third consists of reserve procurement and imbalance settlement and is operated by the local transmission system operator. Short-term markets are important tools to deal with net demand variability in the system, in which the need for flexibility is expressed and its provision is valorized. Due to the ongoing integration of variable renewables in the generation mix, the system’s variability is increasing as a result of the limited controllability and predictability of those resources. As such, these markets become increasingly important. The contribution of this article is a comprehensive up-to-date discussion of the key design parameters and functioning of all three short-term markets, and their impact on the demand for and supply of flexibility. An understanding of the design and its implications is useful to policy-makers who are considering changes to facilitate the integration, availability, or valorization of flexibility, while also contributing to the decision-making of flexibility investors and operators. The geographical scope is the Central Western European region, including the Belgian, French, German, and Dutch market zones.

Optimal Offering and Operating Strategies for Wind-Storage Systems With Linear Decision Rules

The participation of wind farm-energy storage systems (WF-ESS) in electricity markets calls for an integrated view of day-ahead offering strategies and real-time operation policies. Such an integrated strategy is proposed here by co-optimizing offering at the day-ahead stage and operation policy to be used at the balancing stage. Linear decision rules are seen as a natural approach to model and optimize the real-time operation policy. These allow enhancing profits from balancing markets based on updated information on prices and wind power generation. Our integrated strategies for WF-ESS in electricity markets are optimized under uncertainty in both wind power and price predictions. The resulting stochastic optimization problem readily yields optimal offers and linear decision rules. By adding a risk-aversion term in form of conditional value at risk into the objective function, the optimization model additionally provides flexibility in finding a trade-off between profit maximization and risk management. Uncertainty in wind power generation, as well as day-ahead and balancing prices, takes the form of scenario sets, permitting to reformulate the optimization problem as a linear program. Case studies validate the effectiveness of the strategy proposed by highlighting and quantifying benefits w.r.t. other existing strategies.

Corrective Transmission Switching for N-1-1 Contingency Analysis

System operators are required to serve the load in the most cost-effective way while maintaining the integrity of the system and heeding reliability requirements. In the day-ahead market, operators acquire reserves in an attempt to guarantee N -1 reliability; yet, reserve deliverability is not guaranteed. Prior research has shown that the use of transmission switching, or topology control, may help improve reserve deliverability. In this paper, transmission switching is used as a corrective mechanism to help the system achieve N -1-1 reliability, where not only has the system lost a single element, but also it experiences the loss of a second major element after an adjustment period. In an attempt to preserve N -1-1 reliability, for this paper, a day-ahead unit commitment model that acquires supplementary reserves is solved. The day-ahead market solution is then tested for N -1-1 reliability using contingency analysis models with and without transmission switching. The methodology can be employed at the day-ahead time stage to ensure the system has acquired sufficient supplemental reserves. The results demonstrate that not only can corrective transmission switching be beneficial post-contingency without inhibiting the ability to return to N -1 reliability, but it can also help obtain an N -1-1 reliable solution.

A multiple criteria utility-based approach for unit commitment with wind power and pumped storage hydro

The integration of wind power in electricity generation brings new challenges to the unit commitment problem, as a result of the random nature of the wind speed. The scheduling of thermal generation units at the day-ahead stage is usually based on wind power forecasts. Due to technical limitations of thermal units, deviations from those forecasts during intra-day operations may lead to unwanted consequences, such as load shedding and increased operating costs. Wind power forecasting uncertainty has been handled in practice by means of conservative stochastic scenario-based optimization models, or through additional operating reserve settings. However, generation companies may have different attitudes towards the risks associated to wind power variability. In this paper, operating costs and load shedding are modeled by non-linear utility functions aggregated into a single additive utility function of a multi-objective model. Computational experiments have been done to validate the approach: firstly we test our model for the wind–thermal unit commitment problem and, in a second stage, pumped storage hydro units are added, leading to a model with wind–hydro-thermal coordination. Results have shown that the proposed methodology is able to correctly reflect different risk profiles of decision makers for both models.

Integrated day-ahead and hour-ahead operation model of discos in retail electricity markets considering DGs and CO2 emission penalty cost

This paper proposes a new framework for the operation of distribution companies (discos) in the liberalized electricity market environment considering distributed generation (DG) units and carbon dioxide (CO2) emission penalty cost. The proposed short-term framework is a two-stage model. The first stage, namely day-ahead stage, deals with the activities of discos. This stage includes a optimization problem to minimize the costs of distribution company (operational and CO2 emission costs). The first stage is formulated as a mixed integer nonlinear programming (MINLP) framework using the Benders decomposition to determine the decisions of discos to buy power from grid, schedule the DG units and contract with interruptible loads (ILs). The results of the first stage are imposed as the boundary constraints in the second stage which deals with the activities of discos in an hour-ahead period. In the hour-ahead stage, the retailers determine the amount of purchased active and reactive power from the grid and the production of each DG unit in the energy and reserve market keeping in mind its day-ahead decision to maximize the desired short-term profit. Finally, the efficiency of the proposed framework is studied on a case study.

Forecasting Power Prices Using a Hybrid Fundamental-Econometric Model

We investigate the performance of two hybrid forecasting models to predict the next-day base load electricity prices on the APX power exchange for Great Britain. Hybrid models have often been advocated as a synthetic method for forecasting, but it is an open question how well they perform at the day-ahead stage. A conventional hybrid approach combines a fundamental model, formulated with supply stack modeling, with an econometric model using data on price drivers. We extend this model to one based upon logistic smooth transition regression, which represents a regime-switching for periods of structural change. Empirical results for out-of-sample forecasting using both hybrid approaches are discussed and compared to non-hybrid time series forecasting methods.

A Generic Operations Framework for Discos in Retail Electricity Markets

A generic operations framework for a distribution company (disco) operating in a competitive electricity market environment is presented in this paper. The operations framework is a two-stage hierarchical model in which the first deals with disco's activities in the day-ahead stage, the day ahead operations model (DAOM). The second deals with disco's activities in real-time and is termed real-time operations model (RTOM). The DAOM determines the disco's operational decisions on grid purchase, scheduling of distributed generation (DG) units owned by it, and contracting for interruptible load. These decisions are imposed as boundary constraints in the RTOM and the disco seeks to minimize its short-term costs keeping in mind its day-ahead decisions. A case-study is presented considering the well-known 33-bus distribution system and three different scenarios are constructed to analyze the disco's actions and decision-making in this context.