Day-ahead Energy Research Articles

Optimal operation of a residential energy hub participating in electricity and heat markets

The integration of electricity and heat networks provides significant benefits by enhancing system flexibility and improving overall energy efficiency. Energy hubs play an important role in these interconnected systems, facilitating the production, conversion, and storage of energy across different forms. Potential flexible loads that may exist in an energy hub can further optimize its resource utilization and operational stability. In this respect, this paper addresses the day-ahead energy management of a residential complex modeled as an energy hub, incorporating medium-scale generation and storage units, as well as must-run and flexible loads. We also consider energy hub operator’s energy transactions in power distribution system and district heating and aim to obtain the optimal bidding strategy of this profit-driven agent. The negotiations among the energy hub operator, distribution system operator, and district heat network operator are modeled as a single-leader multi-follower Stackelberg game. A Nash Equilibrium of this game can be obtained by modeling the interactions among players as a bi-level optimization problem. The lower-level problems account for multi-period optimal power flow, modeled as an exact AC optimal power flow, and multi-period optimal thermal flow. The upper-level problem models the energy management of the energy hub. Replacing the lower-level problems with their optimality conditions, the optimal bidding of the energy hub operator can be obtained by solving the resulted mixed-integer linear programming problem as a mathematical program with equilibrium constraints. Finally, we numerically evaluate the proposed framework in a case study for a large residential complex participating in a power distribution and a heat network.

A Novel Reinforcement-Learning-Based Compensation Strategy for DMPC-Based Day-Ahead Energy Management of Shipboard Power Systems

A Novel Reinforcement-Learning-Based Compensation Strategy for DMPC-Based Day-Ahead Energy Management of Shipboard Power Systems

The Virtual Power Plant Bidding Strategy Model based on Multi-stage Semi-anticipativity Distributionally Robust Optimization

For virtual power plants (VPPs), the strength of their market competitiveness largely depends on their ability to dispatch various distributed resources. An efficient bidding strategy model that can provide superior decision-making solutions is the key to enhancing this competitiveness. This paper focuses on price-making VPP and constructs an efficient three-period VPP bidding strategy model (3P-VPP) with a bi-level optimization framework for the day-ahead energy market by introducing three-period generator output constraints and generator ramping constraints. Furthermore, to address the uncertainty of renewable resources, we introduce the theory of distributionally robust optimization (DRO) and the concept of semi-anticipativity, refining the 3P-VPP model into a multi-stage DRO VPP bidding strategy model based semi-anticipativity (SADRO-VPP). The model possesses dynamic adaptation capabilities, tailoring the optimal decision-making solutions based on the accuracy of the forecast data. When faced with unreliable prediction data, the model will offer a more robust decision-making plan to guard against potential extreme scenarios. Conversely, when the forecast data is credible, the model adeptly utilizes this information to provide more economical decision-making strategies. The experimental results demonstrate that the 3P-VPP model exhibits higher computational efficiency compared to traditional model, while the SADRO-VPP model provides effective scheduling solutions and bidding strategies for VPP.

Multivariate Deep Learning Long Short-Term Memory-Based Forecasting for Microgrid Energy Management Systems

In the scope of energy management systems (EMSs) for microgrids, the forecasting module stands out as an essential element, significantly influencing the efficacy of optimal solution policies. Forecasts for consumption, generation, and market prices play a crucial role in both day-ahead and real-time decision-making processes within EMSs. This paper aims to develop a machine learning-based multivariate forecasting methodology to account for the intricate interplay pertaining to these variables from the perspective of day-ahead energy management. Specifically, our approach delves into the dynamic relationship between load demand variations and electricity price fluctuations within the microgrid EMSs. The investigation involves a comparative analysis and evaluation of recurrent neural networks’ performance to recognize the most effective technique for the forecasting module of microgrid EMSs. This study includes approaches based on Long Short-Term Memory Neural Networks (LSTMs), with architectures ranging from Vanilla LSTM, Stacked LSTM, Bi-directional LSTM, and Convolution LSTM to attention-based models. The empirical study involves analyzing real-world time-series data sourced from the Australian Energy Market (AEM), specifically focusing on historical data from the NSW state. The findings indicate that while the Triple-Stacked LSTM demonstrates superior performance for this application, it does not necessarily lead to more optimal operational costs, with forecast inaccuracies potentially causing deviations of up to forty percent from the optimal cost.

Hybrid Day-ahead and Real-time Energy Trading of Renewable-based Multi-microgrids: A Stochastic Cooperative Framework

Hybrid Day-ahead and Real-time Energy Trading of Renewable-based Multi-microgrids: A Stochastic Cooperative Framework

Electric vehicle aggregator as demand dispatch resources: Exploring the impact of real-time market performance on day-ahead market

As the integration of variable renewable energy sources (VREs) into power grids escalates, effectively managing the unpredictability of VRE generation becomes crucial. Electric vehicles, utilized as distributed energy storage, emerge as a potential solution. However, it remains unclear whether electric vehicle aggregators (EVAs), acting as demand dispatch resources, have a favorable impact on the overall electricity market, especially when considering their performance in real-time markets during day-ahead energy allocations. Therefore, this paper proposes a decentralized clearing mechanism (DCM) relying on an multi-attribute evaluation model of EVAs for optimal bidding strategies in the day-ahead market. The EVAs multi-attribute evaluation model employs an integration of the Entropy-Analytic Hierarchy Process, allowing for the simultaneous consideration of real-time performance and day-ahead bidding prices of EVAs. Consequently, the decentralized clearing mechanism supports strategic electricity planning in the day-ahead market by incorporating the actual market performance of EVAs. Furthermore, a real-time market bidding model is developed to balance dynamic mismatches between VRE generation and demand, aiming to maximize profits through optimized real-time commitment and real-time dispatch. System simulations on the IEEE39 bus system demonstrate that the DCM enhances overall profits by 18.69% compared to the centralized clearing mechanism.

BEForeGAN: An image-based deep generative approach for day-ahead forecasting of building HVAC energy consumption

This study presents a pioneering approach in building energy forecasting by introducing a novel reformulation framework that transforms the forecasting task into an image inpainting problem. Based upon the fundamental notion that “forecasting is about generating data of the future”, we propose BEForeGAN, an innovative deep generative approach for day-ahead Building HVAC Energy consumption Forecasting based on multi-channel conditional Generative Adversarial Networks (GANs) with U-Net generators. Our method is evaluated using 96,360 hourly HVAC energy consumption records from 11 buildings, demonstrating significant accuracy improvements of 17%∼76% and a substantial variability reduction of 3%∼96% compared to a suite of conventional and deep learning benchmark models across individual-building and zero-shot cross-building forecasting tasks. Notably, BEForeGAN exhibits robustness to noisy inputs, with an increase below 3% in Coefficient of Variation of Root Mean Square Error (CV-RMSE) for each 10% noise increment. This study addresses critical gaps in existing literature by showcasing the untapped potential of GANs as standalone forecasters, advocating for further exploration of two-dimensional (2D) GAN-based methods in building energy forecasting, and emphasising the need for more studies focusing on cross-building forecasting tasks. In conclusion, our findings underscore the transformative impact of GANs in revolutionising building energy forecasting practices, paving the way for enhanced energy-efficient building management and beyond.

Solving a bilevel strategic offering model for a generation company in a hydrothermal energy market: A new convexification approach

This paper proposes a bilevel strategic offering (SO) model suitable for a strategic company (SC) that operates in a hydro-dominated day-ahead energy market. The upper-level model of the proposed SO model maximizes the SC’s profits, while the lower-level model embodies a hydrothermal market clearing (MC). This MC incorporates detailed hydro and thermal constraints for units of both the SC and non-strategic companies (NSC), in addition to the network constraints. The proposed solution approach entails replacing the lower-level model with its corresponding primal and dual constraints, along with a strong duality equality constraint. This leads to a non-convex mathematical programming with equilibrium constraints (MPEC) model, which is then reformulated to feature only a single bilinear term in its structure. Finally, the recast MPEC model is convexified around an initial point and solved iteratively until a convergence criteria is achieved. The model and solution approach proposed are evaluated on an adapted version of the IEEE 24-bus system, exploring scenarios where the SC acts strategically or non-strategically (perfect competition).

Day-ahead optimal scheduling considering thermal and electrical energy management in smart homes with photovoltaic–thermal systems

The environmental impact on the energy sector has become a significant concern, necessitating the implementation of Home Energy Management Systems (HEMS) to enhance the energy efficiency of buildings, reduce costs and greenhouse gas emissions, and ensure user comfort. This paper presents a novel approach to provide optimal day-ahead energy management plans in smart homes with Photovoltaic/Thermal (PVT) systems, aiming to achieve a balance between energy cost and user comfort. This multi-objective problem employs the Non-dominated Sorting Genetic Algorithm III as the optimization algorithm and the Nonlinear Auto-regressive with External Input to forecast the day-ahead meteorological variables, which serve as inputs to predict the PVT electrical and heat production in the thermal resistance model. The HEMS benefits from the time-of-use tariff due to the flexibility provided by the energy storage from a battery bank and a boiler. Furthermore, it performs a load scheduling for 10 controllable loads based on three feature parameters to characterize occupant behavior. A study case analysis revealed a cost reduction of approximately 66% in the solution close to the knee of the Pareto curve (S3 solution). The environmental impact on the energy sector has become a The PVT heat production was sufficient to meet the thermal demand of the showers. The proposed hybrid battery management model effectively eliminated the export of electricity to the grid, reducing consumption during peak periods and the maximum peak demand.

Game-Theory-Based Design and Analysis of a Peer-to-Peer Energy Exchange System between Multi-Solar-Hydrogen-Battery Storage Electric Vehicle Charging Stations

As subsidies for renewable energy are progressively reduced worldwide, electric vehicle charging stations (EVCSs) powered by renewable energy must adopt market-driven approaches to stay competitive. The unpredictable nature of renewable energy production poses major challenges for strategic planning. To tackle the uncertainties stemming from forecast inaccuracies of renewable energy, this study introduces a peer-to-peer (P2P) energy trading strategy based on game theory for solar-hydrogen-battery storage electric vehicle charging stations (SHS-EVCSs). Firstly, the incorporation of prediction errors in renewable energy forecasts within four SHS-EVCSs enhances the resilience and efficiency of energy management. Secondly, employing game theory’s optimization principles, this work presents a day-ahead P2P interactive energy trading model specifically designed for mitigating the variability issues associated with renewable energy sources. Thirdly, the model is converted into a mixed integer linear programming (MILP) problem through dual theory, allowing for resolution via CPLEX optimization techniques. Case study results demonstrate that the method not only increases SHS-EVCS revenue by up to 24.6% through P2P transactions but also helps manage operational and maintenance expenses, contributing to the growth of the renewable energy sector.

Resilient day-ahead microgrid energy management with uncertain demand, EVs, storage, and renewables

Managing microgrid energy presents a complex challenge due to unpredictable renewable sources, fluctuating demand, and diverse equipment like batteries, distributed generators, and electric vehicles. This paper introduces a novel two-step optimization model, the Robust Day-Ahead Scheduling for Enhanced Resilience, tailored for microgrid operations. The model addresses the integration of electronic generation, uncertain demand patterns, and small-scale renewable resources. Detailed formulations optimize microgrid energy use, including strategic battery usage, efficient electric vehicle charging, balancing device utilization, and distributed generation dispatch. This multi-faceted approach aims to minimize costs over 24 h, including energy loss, power purchases, reduced power usage, generator operation, and battery/EV expenses. Employing a column-and-constraint generation (C&CG) algorithm ensures efficient problem solving. The proposed model achieved a significant reduction in operational costs, outperforming existing methods by at least 8%. Notably, it minimized energy purchases, energy losses, and load shedding while improving voltage stability, showcasing its effectiveness in enhancing microgrid performance and resilience.

Day-ahead resilience-economic energy management and feeder reconfiguration of a CCHP-based microgrid, considering flexibility of supply

Many challenges have emerged due to the intense integration of renewables in the distribution system and the associated uncertainties in power generation. Consequently, local management strategies are developed at the distribution level, leading to the emergence of concepts such as microgrids. Microgrids include a variety of heating, cooling, and electrical resources and loads, and the operators' aim is to minimize operation and outage costs. Since significant distribution system outages are typically caused by events such as earthquakes, floods, and hurricanes, microgrid operators are compelled to improve resilience to ensure uninterrupted service during such conditions. A mixed-integer linear programming model is designed in this paper to optimize the energy management and structural configuration of microgrids. This optimization aims to enhance resilience cost, minimizing operation and capital costs as well as power loss and pollution. To achieve these goals, several tools are implemented including reconfiguration, storages, combined cooling, heat and power units, wind turbines, photovoltaic panels, as well as capacitors. Four case studies are defined to prove the developed model efficiency. The first case study focuses on energy management in the microgrid for operation cost minimization. The second case study emphasizes the improvement of resilience alongside energy management, aiming at minimizing costs and enhance resilience. In the third case, the microgrid's reconfiguration capability is also added to the second case. Therefore, this case aims to optimize both energy and structural management within the microgrid to simultaneously enhance resilience and minimize operational costs. Finally, in the fourth case, the problem is studied in a multi-objective approach. By comparing the results, the resilience impact on the operation of microgrids is elucidated. By considering the resilience concept in microgrid operation and based on the results of case 2, it is found that the operating costs are increased by an average of 10.38 %. However, because of reducing resilience costs by an average of 13.91 %, the total cost is reduced by an average of 5.93 % in case 2 compared to case 1. Furthermore, when comparing cases 2 and 3, the reconfiguration effect can be determined. It can be observed that the operating costs are decreased by an average of 4.5 %. Moreover, the resilience cost is decreased by an average of 1.61 %, resulting in an overall reduction of the total objective function by an average of 2.43 % in case 3 compared to case 2.

Energy optimization of the smart residential electrical grid considering demand management approaches

-Many researchers have investigated the optimal energy scheduling to meet the demand regarding sustainable development issues and the economic and technical indices. This study presents the day-ahead operation of a smart residential distribution electrical grid as a bi-stage-multiple-criteria decision-making modeling. The proposed energy optimization is implemented based on optimal demand management in upper-stage and multiple-criteria decision-making in lower-stage. The multiple-criteria problem is modeled from the viewpoint of the grid's operator to optimize energy consumption costs, power losses and demand side comfort. The optimal demand management in upper-stage is coordinated considering price traffic in the upstream grid. The load shifting approach and load interruption approach are presented as optimal demand management for residential consumers. The operation of demand management by using load shifting and load interruption approaches is done via deferrable loads and clippable loads in smart residential homes, respectively. The proposed energy optimization in both stages by improved sunflower optimization (IFSO) is handled, and the TOPSIS method is proposed for the best trade-off of the multiple-criteria decision-making. The day-ahead energy operation is applied on the 33-bus distribution network to investigate the effectiveness of the residential distribution electrical grid considering obtained results from mathematical modeling and demand management approaches.

Benchmarking reservoir computing for residential energy demand forecasting

In the energy sector, accurate demand forecasts are vital but often limited by the available computational power. Reservoir computing (RC) or echo-state networks excel in chaotic time series prediction, with lower computational requirements compared to other recurrent network based methods like LSTMs. Next-generation reservoir computing (NG-RC) is a newer, more efficient variant of classical RC originating from nonlinear vector autoregression and therefore missing the randomness of classical RC. In our study, we evaluate RC and NG-RC for day-ahead energy demand predictions on four data sets and compare it to LSTMs and a naive persistence approach. We find that NG-RC outperforms all other methods when considering the root mean squared error on all data sets but struggles with very small or zero demands. Additionally, it offers a very computationally effective hyperparameter optimization and excels in replicating the inherent volatility and the erratic behavior of energy demands.

Optimal demand response aggregation in wholesale electricity markets: Comparative analysis of polyhedral; ellipsoidal and box methods for modeling uncertainties

This paper presents an optimal framework for demand response aggregators in wholesale electricity markets. Demand response aggregators provide customers with flexible contracts in the proposed model. These contracts allow for hourly load reductions through load curtailment, load shifting, onsite generation utilization, and energy storage systems. The study suggests the price-based self-scheduling model for demand response aggregators, which seeks to maximize the aggregator's payoff for participation in day-ahead energy markets. The proposed model is implemented on a sample demand response aggregator with uncertainty and without uncertainty. Three methods, box, polyhedral, and ellipsoidal, based on robust optimization, are proposed for uncertainty modeling in this work, and their effectiveness is compared with each other. The results show that if the uncertainty is modeled using the polyhedral method, the value of the objective function will deviate by only 22.72 % compared to the case without uncertainty. However, this deviation in box and ellipsoidal methods is 118.48 % and 49.20 %, respectively, which shows the superiority of the polyhedral method compared to the oval and box methods.

Coordinating multiple Power-To-Gas plants for optimal management of e-fuel seasonal storage

Seasonal storage is a key feature of future decarbonized energy systems with a high share of renewable energy integration. Power-to-Gas technologies represent a promising solution to enable such storage. They allow the conversion of surplus renewable electricity into e-fuels and their storage in the long-term. Their utilization enables the integration of the electrical, fuel and heating sectors, by converting electricity into fuels and recovering the waste heat from the process. Nevertheless, to design the most profitable management strategy for such systems, advanced control tools are required. This study introduces a novel control architecture for multiple multi-energy systems that share an e-fuel seasonal storage. Each energy system has its own short-term control logic, based on Model-Predictive Control (MPC), which manages day-ahead energy exchanges, while a long-term MPC controller considers yearly dynamics and the system as a whole. This gives additional constraints to the short-term controllers, which ensure the fulfillment of yearly goals. A multi-temporal and multi-spatial hierarchical control architecture is proposed, which enables optimal seasonal storage management, and its operation is verified in a Model-in-the-Loop configuration. The controller efficiently uses seasonal storage to balance seasonal mismatch between production and demand, resulting in higher utilization of renewable energy, lower emissions and costs.

Cost-Optimal Aggregated Electric Vehicle Flexibility for Demand Response Market Participation by Workplace Electric Vehicle Charging Aggregators

In recent years, with the growing number of EV charging stations integrated into the grid, optimizing the aggregated EV load based on individual EV flexibility has drawn aggregators’ attention as a way to regulate the grid and provide grid services, such as day-ahead (DA) demand responses. Due to the forecast uncertainty of EV charging timings and charging energy demands, the actual delivered demand response is usually different from the DA bidding capacity, making it difficult for aggregators to profit from the energy market. This paper presents a two-layer online feedback control algorithm that exploits the EV flexibility with controlled EV charging timings and energy demands. Firstly, the offline model optimizes the EV dispatch considering demand charge management and energy market participation, and secondly, model predictive control is used in the online feedback model, which exploits the aggregated EV flexibility region by reducing the charging energy based on the pre-decided service level for demand response in real time (RT). The proposed algorithm is tested with one year of data for 51 EVs at a workplace charging site. The results show that with a 20% service level reduction in December 2022, the aggregated EV flexibility can be used to compensate for the cost of EV forecast errors and benefit from day-ahead energy market participation by USD 217. The proposed algorithm is proven to be economically practical and profitable.

Risk-constrained bidding and offering strategy for sector-coupled electricity-hydrogen systems incorporating accessibility level of mobility sector

To enhance the economic viability of integrated energy systems, it is important to balance risk and reward while ensuring operational flexibility and compliance with regulatory constraints. Developing an integrated risk measurement method for energy trading in energy markets that complies with regulatory constraints adds another layer of complexity, especially when dealing with large-scale heterogeneous energy systems, such as those dominated by green hydrogen. Ensuring adherence to market regulations, grid codes, and flexible requirements requires a thorough understanding of legal frameworks and policy considerations, as well as integrating regulatory constraints into the bidding/offering strategies. To address these challenges and enable the profitable development of integrated electricity-hydrogen energy systems (IEHSs), this paper develops a risk-constrained bidding/offering self-scheduling strategy for IEHSs that allows for simultaneous trading in day-ahead energy and reserve electricity markets. The developed self-scheduling strategy regulates hydrogen and power flow in a centralized manner, taking into account the operational limits of the power sector, hydrogen fueling stations (HFSs), electric vehicle parking lots, fuel cell vehicle parking lots, and renewable energy sources. As a salient feature, the important human-related factors in the mobility sector are included in the proposed strategy to motivate private owners to collaborate with IEHSs and to accommodate the charging demand of vehicles with electricity market signals. To obtain the robust day-ahead schedule, economic risks associated with renewable power generation, calls for reserve deployment, and electricity market prices are taken into consideration. Towards this end, a stochastic adaptive robust optimization approach is proposed to control the risk of profit variability, which is solved by a nested column-and-constraint generation algorithm. The effectiveness and feasibility of the proposed strategy are verified using the modified IEEE 14-bus test system, as a standard scale of local IEHSs. The numerical results highlight the synergies impact between HFSs and the power sector, resulting in an increase in the profit of the IEHS from $101.654k to $275.078k. Furthermore, the IEHS can improve its profit margin and competitiveness by employing the tri-level stochastic adaptive robust optimization approach. This approach ensures that the IEHS is resistant to sudden changes in real-time interactions, even with an 8.14% increase in the imbalance cost.

Market equilibrium with strategic pricing and strategic constraints in renewable energy: the role of private energy storage

With the increasing prevalence of renewable energy (RE) companies equipped with private energy storage (ES) systems, a dual capability emerges to offer strategic pricing and strategic constraints in market competition. Specifically, these RE companies can strategically leverage their own private ESs to modulate the variability of RE output limits and introduce modified constraints within the market. To examine these new strategic behaviors and the resulting market equilibria, we introduce an innovative bilevel strategic behavior model. The upper level of the model delineates the strategy for RE profit maximization through the imposition of strategic constraints and pricing schemes, while the lower level calculates the revenue outcomes for all entities in the day-ahead energy market clearing. The integration of the bilevel models from all strategic entities leads to the formulation of a new equilibrium problem with equilibrium constraints (EPEC), the solution of which indicates a novel market equilibrium. The impacts of these market equilibria on critical system operation metrics are then evaluated across two representative market mechanisms. Our numerical experiments reveal that RE exhibits low sensitivity to the private ES’s cost, suggesting that the behavior of imposing strategic constraints may be widespread among RE companies owning private ESs. Furthermore, the introduction of strategic constraints enhances the competitiveness of RE, significantly affecting social welfare, energy pricing, and RE integration rate. The study concludes with insights that could inform practical market transactions and system operations.

Optimal scheduling of electricity-hydrogen coupling virtual power plant considering hydrogen load response

With the rapid development of hydrogen production by water electrolysis, the coupling between the electricity-hydrogen system has become closer, providing an effective way to consume surplus new energy generation. As a form of centralized management of distributed energy resources, virtual power plants can aggregate the integrated energy production and consumption segments in a certain region and participate in electricity market transactions as a single entity to enhance overall revenue. Based on this, this paper proposes an optimal scheduling model of an electricity-hydrogen coupling virtual power plant (EHC-VPP) considering hydrogen load response, relying on hydrogen to ammonia as a flexibly adjustable load-side resource in the EHC-VPP to enable the VPP to participate in the day-ahead energy market to maximize benefits. In addition, this paper also considers the impact of the carbon emission penalty to practice the green development concept of energy saving and emission reduction. To validate the economy of the proposed optimization scheduling method in this paper, the optimization scheduling results under three different operation scenarios are compared and analyzed. The results show that considering the hydrogen load response and fully exploiting the flexibility resources of the EHC-VPP can further reduce the system operating cost and improve the overall operating efficiency.