Day-ahead Energy Research Articles

Physically consistent deep learning-based day-ahead energy dispatching and thermal comfort control for grid-interactive communities

Grid-interactive communities are an emerging solution for optimal energy dispatching, improving grid stability, and enhancing the usage of on-site renewable energy by leveraging community flexibilities. This work introduces a novel Physically Consistent Deep Learning (PCDL)-based optimization framework for day-ahead energy dispatching and thermal comfort control within grid-interactive communities. Specifically, the PCDL model is developed to capture the thermal dynamics within the community while ensuring strict adherence to physics consistency. This consistency is defined with the aim of generating physically viable solutions in response to modified system inputs. Subsequently, the PCDL model is utilized for load estimation and indoor climate prediction within a proposed scheduling and control strategy: (1) an optimal energy dispatching plan is calculated based on the day-ahead grid market; (2) a real-time model predictive control (MPC) method is applied to minimize the utilization error and indoor comfort constraint violations caused by following the scheduled energy dispatching plan. A simulation case of a residential hall community at Cornell University with on-site renewable energy resources is implemented to demonstrate the effectiveness of the proposed approach. Comparative simulations, which include the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Gaussian Process (GP) regression models, confirm the performance advantage of capturing community thermal dynamic and control-oriented generalization ability when using the PCDL model. These results leads to notable improvements in indoor thermal comfort control, ranging between 65.4 and 68.7%, 63.6–79.3%, and 60.4–62.2% for each comparative model, respectively. Moreover, by harnessing community demand flexibility, we achieve energy cost savings between 29.5 and 39.7% compared to the baseline controller.

Distributionally robust economic scheduling of a hybrid hydro/solar/pumped-storage system considering the bilateral contract flexible decomposition and day-ahead market bidding

A hybrid renewable energy system (HRES) utilizes the coaction of diverse energy to enhance energy efficiency while improving economic benefit. Under the paradigm of the electricity market, the HRES decomposes bilateral contract (BC) signed in the medium to long-term market and bids in the spot market are a promising way to get more revenue while ensuring basic profits by exploiting the synergy of different markets. However, the scheme of BC decomposition and the uncertainties of renewable energy affect the revenue of HRES scheduling. This paper proposes an optimal scheduling model for HRES to perform the BC flexible decomposition while bidding in the day-ahead energy market to maximize total revenue. The HRES is a real system located in Southwest China, and its constituent units are a pumped storage, two hydropower, and two solar stations. The Wasserstein metric ambiguity set is adopted to portray the uncertainty of available solar power and the forecasted market price error. Then, the proposed model is formulated as a distributionally robust chance-constrained scheduling model. Finally, the conditional value-at-risk approximation and strong duality are employed to reformulate this model into a tractable problem. The numerical results show that the proposed model can increase the total profit of HRES by 2.26 % (i.e., 2.56 × 104 DKK) compared to the case of fixed BC decomposition and bidding. Moreover, the impact analysis related to the ambiguity set and risk tolerance of uncertainty on total revenue of the proposed model is also presented, which provides the measure for the HRES to trade-off the robustness and economy of the solution.

Day-ahead multi-modal demand side management in microgrid via two-stage improved ring-topology particle swarm optimization

The role of an energy management system is crucial today. However, the heavy reliance on fossil fuels as well as the growing gap between electricity demands and energy power generation have resulted in various global challenges, including energy shortages, high utility bills and greenhouse gas emissions. To address this issue, this paper presents a practical and comprehensive microgrid model that combines day-ahead multi-modal demand side management (DSM) and energy storage (ES) operation. The model aims to provide multiple optimal or near-optimal DSM suggestions to users, increasing their willingness to respond the suggestions and fully leveraging the benefits of DSM. Besides, the ES operation works in conjunction with DSM as an energy buffer for generated power, ensuring that user demands are always met. Particle swarm optimization(PSO) is improved to optimize the DSM model due to its merits, including simplicity, population-based structure, and effective learning mechanism. To balance the two key capabilities of PSO well, the exploration ability of PSO is enhanced by index-based ring topology, ensuring that the entire particle swarm can evenly diverge across the search space, while the exploitation ability of each sub-swarm is improved using a greedy search strategy, empowering each sub-swarm can effectively exploits their respective surroundings. To demonstrate the effectiveness of the proposed model, four different DSM strategies are designed in MG system for different purposes of cost saving, carbon emissions reduction, cost saving with load fluctuations stabilization, and emissions reduction with load fluctuation stabilization, respectively. Numerical experiments reveal advantages of the proposed ir3PSO that can search for qualified solutions with better diversity and higher accuracy in a single run. In addition, detailed sensitivities of key parameters, including load participation level and user acceptance, are also analyzed for reference by decision-makers.

An Optimal Operation Strategy for Collaborative Flexibility Provision of a Carbon Capture and Utilization Process With Wind Energy

Improving power system flexibility by responsive demand is essential for integrating wind energy with a high level of variability in power systems. Carbon dioxide-based chemical processes as energy-intensive industrial loads may offer a vast potential of new forms of flexible operation due to their existing control infrastructure and storage capabilities. However, a collaborative decision model is needed for optimal energy sharing among the chemical plant and the grid under the variations and uncertainties of wind power. This study develops an optimal two-stage stochastic programming model for a novel flexible operation strategy of the chemical process coupled with wind turbines. In the proposed control scheme, a small-scale wind farm provides the power input of a chemical plant. Wind turbines are connected to the grid and actively participate in the day-ahead energy and reserve markets, considering the chemical plant as a source of flexibility. An equivalent scenario-based model of the proposed optimization problem is suggested using the Group Method of Data Handling (GMDH) for a data-driven prediction of stochastic variables. Simulation results demonstrate the effectiveness and significance of the proposed approach for an optimal and collaborative contribution in ancillary market of a carbon dioxide-based chemical plant supplied by wind energy.

Fast energy transition as a best strategy for all? The nash equilibrium of long-term energy planning strategies in coupled power markets

The research links energy system development planning to day-ahead energy markets, market coupling, and renewable energy integration, with a novel approach based on game theory. A two-level method is suggested for long-term energy strategy decisions. In the first stage, four hypothetical zones are simulated using an energy system's operation optimization model, emphasizing electricity flows. Game theory is employed in the second stage to select the best market-coupled zone strategy. A game reflecting transition dynamic, renewables integration, and demand response is formulated in the second step of the approach, where each of the four zones have two possible strategies (fast or slow transition), resulting in 16 sets of strategies (scenarios). Results demonstrate the feasibility of determining a Nash equilibrium, enhancing decision-making compared to prior methods. For the observed hypothetical case, a pure Nash equilibrium is found, where all zones opt for a rapid energy transition.

Adaptive robust scheduling optimization of a smart commercial building considering joint energy and reserve markets

With the increase in renewable energy penetration and the widespread use of electric vehicles, the uncertainty of controllable resources adversely affects the economic scheduling of smart buildings. Meanwhile, the smart building can take advantage of its flexibility through multiple markets mechanism. Therefore, this paper proposes an optimal scheduling model for the smart commercial building, including electric vehicle charging, which partakes in the day-ahead energy reserve markets. Firstly, the behavior of electric vehicles is obtained through Monte Carlo simulation. This paper categorizes electric vehicles according to their behavioral characteristics and proposes corresponding scheduling strategies. Then, the joint energy and reserve markets mechanism is introduced, and the robust adaptive optimization (ARO) method is employed to deal with uncertainties. The proposed model is a three-level and two-stage model, which can be solved by the column and constraints generation algorithm. Finally, the economy and feasibility of the proposed model are validated through a case study. Results show that the ARO method saves 3%–4% of expenses more than the pure robust optimization method, and the proposed joint markets mechanism can increase profits by 1%–4%.

Two-stage stochastic-robust model for the self-scheduling problem of an aggregator participating in energy and reserve markets

This paper addresses a two-stage stochastic-robust model for the day-ahead self-scheduling problem of an aggregator considering uncertainties. The aggregator, which integrates power and capacity of small-scale prosumers and flexible community-owned devices, trades electric energy in the day-ahead (DAM) and real-time energy markets (RTM), and trades reserve capacity and deployment in the reserve capacity (RCM) and reserve deployment markets (RDM). The ability of the aggregator providing reserve service is constrained by the regulations of reserve market rules, including minimum offer/bid size and minimum delivery duration. A combination approach of stochastic programming (SP) and robust optimization (RO) is used to model different kinds of uncertainties, including those of market price, power/demand and reserve deployment. The risk management of the aggregator is considered through conditional value at risk (CVaR) and fluctuation intervals of the uncertain parameters. Case studies numerically show the economic revenue and the energy-reserve schedule of the aggregator with participation in different markets, reserve regulations, and risk preferences.

A stochastic analysis of the energy and reserve operation for battery storage-assisted prosumer aggregator in the Southwest Power Pool Market

To facilitate the integration of renewable energy in electric power systems, the installed capacity of distributed renewable energy sources has been increased rapidly worldwide in recent years, and the prosumers and prosumer aggregators have become important players in smart grid. To maximize the economic benefits of a prosumer aggregator in power markets, a stochastic model used for generating the day-ahead (DA) energy and operating reserve scheduling strategies is proposed. The uncertainties associated with solar power productions, demands and prices in energy and operating reserve markets are handled via stochastic optimization technique. To manage the potential risks faced by the prosumer aggregator, the conditional value at risk is adopted to measure and manage its potential low profits or losses. Finally, case studies are performed by using the data in the energy and operating reserve markets operated by the Southwest Power Pool (SPP), and the optimal energy and operating reserve bids are generated effectively by solving the proposed model. The values of the operating reserve markets and battery storages are analyzed in detail via simulation, and it shows that higher reserve prices and larger battery storage capacity would lead to higher expected profit and lower risks for the prosumer aggregator.

Amalgamating TSO and DSO Energy Markets Through Minimal Data Exchange-Based Framework

Integration of distributed energy resources (DERs) in the distribution system (DS) raises the need to have a trading platform for their energy products. The evolution of distribution system operators (DSOs) facilitates the participation of DERs in the energy market at DS level. To utilize the potential of DERs in DS at the transmission level, an iterative coordinated market operations (CMO) scheme for a day-ahead energy market is proposed in this paper. In the proposed scheme, coordination between the transmission system operator and DSOs is established with limited exchange of information to realize an efficient and economical market framework. The communication procedure and time interval for data exchange during various intermediate operations, and convergence criteria of the proposed iterative scheme are defined. The main goal is to accommodate CMO into the existing market clearing mechanism with minimal changes. Case studies on three test systems demonstrate the viability of proposed scheme and the improvement in overall economics in comparison to the conventional method of market clearing as practiced or proposed in literature.

A distributed robust ADMM-based model for the energy management in local energy communities

Increasing the number of participants in energy communities leads to a new challenge in power systems, which is finding the optimal strategy for community members. Accordingly, this paper presents a distributed model for determining the optimal energy trading strategy of community participants such as buyers, sellers, and the community manager (CM). In the proposed model, the local day-ahead energy market, peer-to-peer (P2P) contracts, and the power grid are considered for trading energy between participants as well as compensating for power shortages/surpluses in the community. To model the uncertainty of PV generation, and selling/buying prices of the distribution network, the robust optimization (RO) approach is used. According to the defined budget of uncertainty, the optimal strategies of community members are determined based on the worst-case realizations of uncertain parameters. To decrease the solution time, the distributed optimization method is addressed. Accordingly, the augmented Lagrangian relaxation (ALR) and the alternating direction method of multipliers (ADMM) methods are used to decompose the optimization problem. The performance of the proposed model is evaluated through a case study. Simulation results demonstrate that the proposed model reduces the solution time of energy management problem in communities, significantly.

Risk-averse and flexi-intelligent scheduling of microgrids based on hybrid Boltzmann machines and cascade neural network forecasting

The future of energy flexibility in microgrids (MGs) is steering towards a highly granular control of the end-user customers. This calls for more highly accurate uncertainty forecasting and optimal management of risk and flexibility options. This paper presents a novel data-driven model to optimize the operation of MGs based on a risk-averse flexi-intelligent energy management system (RFEMS), considering the rising challenge of global climate change. It considers the presence of renewables, a diesel generator, and flexibility resources (FRs) containing a demand response program (DRP), distributed electric vehicles (EVs), and electric springs (ESs). In the first phase, the proposed model, by means of a novel hybrid deep-learning (DL) model, forecasts uncertain parameters associated with wind and solar generations, load demand, and day-ahead energy market price. The architecture of the proposed hybrid forecasting model is composed of several stacked restricted Boltzmann machines and a cascade neural network. In the second phase, the MG operator (MGO), based on the obtained uncertainty forecasting results, in the context of a hybrid risk-controlling model, optimizes the MG operation using the provided demand-side flexibility. The proposed optimization problem is linearized stochastic programming with robust concepts, subject to AC optimal power flow constraints, MG flexibility restrictions, and operating limits of local resources. Finally, the efficiency of the proposed RFEMS by using real German datasets on a 33-bus test MG is analyzed. Numerical results demonstrate the superior performance of the proposed forecasting model over several hybrid DL models. In particular, the root mean square error (RMSE) index for wind, solar, load, and price forecasting is improved by 53.35%, 73.24%, 80.24%, and 58.1%, respectively. Further analysis of the proposed RFEMS reveals that operating indices in two 33-bus and 69-bus test networks are significantly improved. It paves pathways to risk-averse, flexible, and economic operation of smart active distribution networks.

Comparison of evolutionary algorithms for solving risk-based energy resource management considering conditional value-at-risk analysis

Energy management systems must evolve due to the widespread use of distributed energy resources in modern society. In fact, with the current high penetration of renewables and other resources like electric vehicles, the challenge of managing energy resources becomes more difficult. Uncertainty and unpredictability from distributed resources open the door for unique undesirable situations, often known as extreme events. Despite the low likelihood of occurrence, such severe events represent a significant risk to an aggregator’s resource management, for example. In this paper, we propose a day-ahead energy resource management model for an aggregator in a 13-bus distribution network with high penetration of distributed energy resources. In the proposed model, we consider a risk-based mechanism through the conditional value-at-risk method for risk measurement of these extreme events. Due to the complexity of the model, we also propose the use of evolutionary algorithms, a set of stochastic search algorithms, to find near-optimal solutions to the problem. Results show that implementing risk-averse strategies reduces the cost of the worst scenario and scheduling. From the tested algorithms, ReSaDE provides the solutions with the lowest cost, which is an improvement from previous work, and a reduction of around 13% in the worst-scenario costs comparing a risk-neutral approach to a risk-averse approach.

Stochastic Coordinated Management of Electrical–Gas–Thermal Networks in Flexible Energy Hubs Considering Day-Ahead Energy and Ancillary Markets

This paper presents an optimal operation framework for electrical, gas, and thermal networks in the presence of energy hubs (EHs), so that EHs can benefit from day-ahead ancillary and energy markets. Therefore, to consider the goals of network operators (optimal operation of networks) and EHs (optimal operation in markets), the proposed model is developed in the form of a bi-level optimization. Its upper-level formulation minimizes the expected energy loss in the proposed networks based on the optimal power flow constraints and technical limits. At the lower-level problem, maximizing the expected profit of EHs in day-ahead energy and ancillary markets (including reactive and reserve regulation) is formulated based on the operational model of resources, storage devices, and responsive load in the EH framework, and the flexible constraints of EHs. This scheme includes the uncertainties of load, market price, renewable energy resources, and mobile storage energy demand, which uses the point estimation method to model them. Karush–Kuhn–Tucker is then used to extract the single-level model. Finally, by implementing the proposed scheme on a standard system, the obtained numerical results confirm the capability of the proposed model in improving the network’s operation and economic status of EHs. As a result, the proposed scheme is able to decrease operation indices such as energy losses, voltage drop, and temperature drop by approximately 28.5%, 39%, and 27.8%, respectively, compared to load flow analysis. This scheme can improve the flexibility of EHs, including non-controllable sources such as renewable resources, by nearly 100% and it obtains considerable profits for hubs.

A Market Framework for a 100% Renewable Energy Penetration Spot Market

The global installed capacity of renewable energy sources (RESs) is increasing rapidly. The 100% RESs power supply is being pursued. When renewable energy penetration tends to be 100%, the source-load coordination problem becomes extreme. The problems of market failure caused by the near-zero marginal cost of RESs and the scarcity pricing of flexible resources (FRs) participating in the rapid energy deviation balance need to be solved. Accordingly, a spot market framework including the day-ahead renewable energy temporal capacity market and the real-time energy sharing market is proposed in this paper. The RESs and FRs are decoupled to ensure that the homogenous commodities compete together. In the day-ahead stage, to obtain the locational marginal price with the guiding role of resource allocation, the fixed cost of the RESs is apportioned to obtain the spatial-temporal capacity price. In the real-time stage, a price-driven distributed energy sharing mechanism is proposed. It is proven that the sharing mechanism can achieve optimum social welfare and satisfy incentive compatibility. The simulation verifies that the new framework can solve market failure. The real value of FRs is discovered when a price consensus is reached.

A Risk-Averse Adaptive Stochastic Optimization Method for Transactive Energy Management of a Multi-Energy Microgrid

This paper proposes a new energy management method for a multi-energy microgrid (MEMG) which supplies both electrical and thermal energies. Based on the transactive energy (TE) concept, the problem is formulated as a Stackelberg game-theoretic bi-level optimization model. The MEMG operator optimizes the energy scheduling and pricing strategies at the upper level, and the industrial, commercial and residential agents optimize their energy trading strategies at the lower level. The equivalent single-level mixed-integer linear program (MILP) reformulation is then derived for computational tractability. To coordinate the strategies made in the day-ahead and intra-day energy markets, an adaptive stochastic optimization (SO) approach is adopted, by which a day-ahead stochastic MILP and an intra-day deterministic model are formed. Furthermore, the conditional value-at-risk (CVaR) measure is incorporated in the day-ahead stage for risk aversion of the MEMG operator towards uncertainties. To solve the models, an adaptive Progressive Hedging (PH) algorithm is developed to decompose the day-ahead stochastic MILP into multiple scenario-based subproblems which can be solved in parallel, and an outer approximation (OA) algorithm is adopted in the intra-day stage to linearize the bilinear objective function. Finally, the simulation results verify the effectiveness of the proposed energy management method and the efficiency of the solution algorithms.

A data-driven Stackelberg game approach applied to analysis of strategic bidding for distributed energy resource aggregator in electricity markets

In the context of the participation of distributed energy resource (DER) aggregators in day-ahead energy and reserve markets, most of the existing analysis models of strategic bidding behavior of DER aggregators ignore the consideration of the safe operation of distribution systems. To this end, this paper builds a one-leader multi-follower Stackelberg game model considering the safety checking of distribution systems with the discrete features of capacitor banks and tap ratio of the substation transformer in the lower level. Mathematically, this model constitutes a bi-level mixed-integer nonlinear programming (BMINLP) problem. We convert the BMINLP model to a bi-level mixed-integer linear programming (BMILP) model based on KKT conditions, strong duality theorem, and big-M approach, which is still difficult to be solved directly. As a result, we propose a customized data-driven algorithm based on sampling without tuning hyper-parameters and an improved linear surrogate function method, to accurately fit the corresponding constraints of the lower-level model of BMILP problem, and then transform the BMILP problem into a single-level mixed-integer linear programming (MILP) problem to solve. The simulation of data-driven algorithm and comparison with the particle swarm optimization algorithm shows the high practicability of the bi-level Stackelberg game model and the effectiveness of the data-driven algorithm.

Benefits of thermal load forecasts in balancing load fluctuations through thermal storage

Planning and managing the operation of cogenerative plants (CHP) is increasingly becoming an industrial necessity because of the participation of CHP plants in the day-ahead energy market and the need to deal with heat demand fluctuations. Short-term operational planning usually considers power and heat demand forecast, which can widely fluctuate daily and seasonally, to maximise the net revenue and fulfil the heat requirements. In this framework, a Thermal Storage (TS) can balance day-night fluctuations due to outdoor temperature, as well as unexpected energy surplus and deficiencies caused by heat demand forecast errors, thus giving the CHP plant more operational flexibility. In this paper, the capability of a TS to balance thermal load fluctuations and forecast errors is investigated when a Machine Learning (ML) thermal load forecast for short-term predictions up to 48 h is used. The TS is modelled as a layered storage tank with perfectly mixed layers. Weather and consumption data from 2018 to 2020 related to a large greenhouse powered by a CHP plant located in Tuscany were used as a case study to perform the training and validation of the forecast model as well as to analyse the TS capability in balancing fluctuations with volumes ranging from 500 up to 10,000 m3. Several ML algorithms were used and compared against a naive prediction based on load persistency. Support Vector Machine (SVM) resulted as the best-performing algorithm. Using SVM leads to better exploitation of the TS capacity, compared to persistence, leading to a more regular State of Charge (SOC) trend and allowing the system to operate within expected conditions up to 80 % of the year. In contrast, a more naïve forecast approach brings to relevant volume size increase to achieve equal performance. Finally, a more accurate forecast reduced the TS size to 50 %, potentially cutting the investment and operational costs compared to the load persistency forecast strategy.

Day-ahead energy management in smart homes with demand response and electric vehicle participation

Day-ahead energy management in smart homes with demand response and electric vehicle participation

Day-ahead solar photovoltaic energy forecasting based on weather data using LSTM networks: a comparative study for photovoltaic (PV) panels in Turkey

Day-ahead solar photovoltaic energy forecasting based on weather data using LSTM networks: a comparative study for photovoltaic (PV) panels in Turkey

When Hierarchical Federated Learning Meets Stochastic Game: Toward an Intelligent UAV Charging in Urban Prosumers

Unmanned aerial vehicles (UAVs) nowadays are developing rapidly for various applications such as UAV taxis and delivery drones. However, the limited battery energy restricts the flight distance of the UAVs. Thus, urban prosumers equipped with drone recharge stations are introduced to provide charging services for the UAVs. In this article, firstly, a day-ahead energy scheduling problem for UAV charging-enabled urban prosumers is studied, where the objective is to maximize the overall energy satisfaction of the prosumers with ensuring the quality of service (QoS) of the charged UAVs. Specifically, to deal with the considered problem, we decompose it into two stages: 1) day-ahead energy requirement data prediction stage, and 2) energy scheduling stage per prosumer. Thus, secondly, a joint method based on hierarchical federated learning (HFL) on long short-term memory (LSTM) architecture (HFL-LSTM) and stochastic game-based multi-agent double deep Q-learning (MADDQN) with community agent-independent approach is proposed. In particular, the HFL-LSTM approach is leveraged to forecast each prosumer’s energy requirement data without centralized collecting local prosumers’ data such that to protect data privacy. Then, the stochastic game is adopted to analyze the formulated problem, aiming to find the Nash equilibrium strategy. Afterward, MADDQN with a community agent-independent method is utilized to achieve the best energy scheduling strategy per prosumer. Finally, the experimental results demonstrate the superiority of the proposed joint method that can achieve the lowest mean squared error with the value of 0.0152 and the highest energy satisfaction (36388) achieved by the Nash equilibrium policy compared with the benchmarks.