Optimal Energy Scheduling Research Articles

Optimal HVAC Energy and Regulation Reserve Scheduling in Power Markets

This paper considers the optimal scheduling of the hourly energy consumed and regulation reserve capacity offered by a Heating, Ventilation and Air-Conditioning (HVAC) system participating in a day-ahead electricity market. We formulate an Integrated hourly energy and regulation reserve Scheduling and Deployment (ISD) problem using high fidelity models of the HVAC and the building structure's thermal storage property. The ISD problem optimizes the hourly costs and benefits resulting from the power consumed, the regulation reserve capacity offered, the occupant thermal comfort utility, and the expected Intra-Hour Costs (IHCs) due to the HVAC's imperfect tracking of the regulation signal broadcasted by the Independent System Operator every few seconds. Addition of the IHCs to the objective function is the major innovation of this paper. It enables optimal HVAC scheduling to internalize expected regulation signal tracking error cost traded-off against incremental occupant thermal discomfort that may result from perfect tracking. The cost causation circle closes by noting that a high hourly regulation reserve offer may result in higher expected IHCs by increasing the associated tracking error and the incremental occupant discomfort. The paper's innovation is achieved by (i) estimating an analytic function of the hourly HVAC scheduling decisions and state variables that approximates the expected IHCs, and (ii) including this exogenously estimated analytic IHCs function in the objective of the hourly ISD optimization problem. Non-convexities and non-linearities introduced to the HVAC optimization problem resulting from high modeling fidelity and the inclusion of the expected IHCs, are addressed efficiently through piecewise convex relaxations that provide tight optimality bounds. Extensive numerical results are finally provided to demonstrate the applicability and performance of our ISD problem formulation on a realistic office building.

Optimal Control and Energy Scheduling for Linear-Quadratic-Gaussian System with Energy Harvesting Constraints

Optimal Control and Energy Scheduling for Linear-Quadratic-Gaussian System with Energy Harvesting Constraints

Microgrid Optimal Energy Scheduling Considering Neural Network Based Battery Degradation

Battery energy storage system (BESS) can effectively mitigate the uncertainty of variable renewable generation. Degradation is unpreventable and hard to model and predict for batteries such as the most popular Lithium-ion battery (LiB). In this paper, we propose a data driven method to predict the battery degradation per a given scheduled battery operational profile. Particularly, a neural network based battery degradation (NNBD) model is proposed to quantify the battery degradation with inputs of major battery degradation factors. When incorporating the proposed NNBD model into microgrid day-ahead scheduling (MDS), we can establish a battery degradation based MDS (BDMDS) model that can consider the equivalent battery degradation cost precisely with the proposed cycle based battery usage processing (CBUP) method for the NNBD model. Since the proposed NNBD model is highly non-linear and non-convex, BDMDS would be very hard to solve. To address this issue, a neural network and optimization decoupled heuristic (NNODH) algorithm is proposed in this paper to effectively solve this neural network embedded optimization problem. Simulation results demonstrate that the proposed NNODH algorithm is able to obtain the optimal solution with the lowest total cost including normal operation cost and battery degradation cost.

Stochastic energy management of a multi-microgrid system with battery/ supercapacitor energy storages considering demand response and transactive energy

This paper proposes stochastic energy management for an electrical system consisting of a multi-microgrid and a distribution company (DISCO). The objective function aims at finding an optimal day-ahead energy schedule of dispatchable resources, energy storages, and demand response in the presence of intermittent renewable energy sources. Two different energy storage devices, i.e., battery and supercapacitor (SC), are modeled in the system to provide both high power and energy density. Wind speed, solar irradiation, and electrical demand uncertainties have been modeled using probability density functions of uncertain parameters, and the number of scenarios has been decreased using mixed-integer linear programming (MILP) based scenario reduction with a novel characteristic. In the proposed model, the DISCO, as a leader, attempts to maximize its profit while the microgrids (MGs), as followers, tend to minimize their operational costs. Karush-Kuhn-Tucker, strong duality, and Fortuny-Amat transformation are used to convert the non-linear bi-level problem into a linear single-level problem. The proposed model was tested on a hypothetical distribution system and the results showed the efficiency of the model in reducing operational costs of MGs by 7.5%, increasing the DISCO profit by 6.9%, and improving the reliability of the system by reducing the energy not supplied.

Energy scheduling in a smart energy hub system with hydrogen storage systems and electrical demand management

The energy price uncertainty in energy markets is the main challenge for achieving economic modeling of energy consumption and generation. This paper proposes optimal energy scheduling in a smart energy hub (SEH) under the uncertainty of electricity prices in the day-ahead. The proposed optimization approach is implemented by a two-layer interval approach for minimizing energy generation cost. In first layer optimization, demand side management (DSM) for electrical demand subject to the optimal level of electrical consumption is implemented. In second-layer optimization, the interval approach is used for modeling uncertainty. The energy generation cost is converted to bi-objective functions including average and deviation costs with conflicting nature than each other. Also, the multi-performance of the hydrogen storage system based on electrical and natural gas generation is considered for managing uncertainties in second layer optimization. Since the interval optimization approach is proposed in the second layer, minimizing the average cost and deviation cost are formulated as bi-objective functions by the augmented epsilon-constraint method. In the following, the LINMAP procedure is utilized to achieve maximum compatibility and the best trade-off consequence in the Pareto frontier of the multi-objective functions. Finally, the proposed optimization approach is modeled as a numerical simulation in the case studies to verify obtained results. The participation of the DSM and the hydrogen storage systems leads to minimizing the average and deviation costs by 3.03 % and 3.16 % in comparison with non-participation.

Synergies between Carnot battery and power-to-methanol for hybrid energy storage and multi-energy generation

Power-to-methanol (PtMe) technologies and Carnot batteries are two promising approaches for large-scale energy storage. However, the current low efficiency and inadequate profitability of these two technologies, especially concerning green methanol production, pose challenges for their industrial implementation. One solution is to integrate these two technologies to augment the material and energy utilization efficiency through dynamic operational coordination and multi-energy production. This study systematically investigates the synergies of integrating CO2 energy storage (CES) and PtMe for combined heating, power, and methanol generation, aiming to enhance system's efficiency and economic viability. In this context, a novel hybrid energy-driven polygeneration system (CES-PtMe) is developed, featuring a highly integrated methanol production process and a newly proposed CES configuration with heating input and output capabilities. Detailed system simulations assess its performance under steady state, while nonlinear programming models are formulated for optimal storage sizing and energy scheduling considering fluctuating renewable energy. Various scenarios are set to examine the techno-economic potential of the CES-PtMe system, using renewable data from Gansu, China as a case study. The results indicate that the system can achieve a high efficiency of nearly 80% with a renewable penetration exceeding 77%. By incorporating optimal energy scheduling throughout the year, the system shows economic viability in the context of the current fossil energy market. While it has a limited ability to meet user energy demands, the proposed system retains economic benefits compared to standalone technologies.

Energy Optimization Management Strategy for DC Nano-Grid Cluster With High Comprehensive Energy Efficiency

Nano-grid (NG) clusters (NGCs) have the potential to serve as as an essential configuration for future low-voltage distribution networks. To address the comprehensive energy efficiency (CEE) problem over a longer time horizon, this paper proposes an energy optimization management strategy. First of all, three typical models are developed, namely equivalent circuit model, cluster loss model and energy sharing model. In the modelling, influences of port characteristics and power transmission losses of interconnecting converters are considered to more accurately describe the cluster power flow. Then, an optimal energy scheduling mechanism with high CEE is proposed based on the three established models. The strategy decomposes the complex CEE problem into three interdependent sub-problems, corresponding to the three established models. First, ensuring the bus power quality and battery requirements are satisfied based on the proposed equivalent circuit model. Second, improving cluster self-sufficiency based on cluster energy sharing model. Third, achieving high energy transfer efficiency based on cluster loss model. Extensive simulation results demonstrate that the CEE under the proposed strategy achieves significant performance improvements, with increases of 8% and 32% in energy transfer efficiency and energy self-sufficiency respectively.

Integrated Energy System Based on Isolation Forest and Dynamic Orbit Multivariate Load Forecasting

Short-term load forecasting is a prerequisite for achieving intra-day energy management and optimal scheduling in integrated energy systems. Its prediction accuracy directly affects the stability and economy of the system during operation. To improve the accuracy of short-term load forecasting, this paper proposes a multi-load forecasting method for integrated energy systems based on the Isolation Forest and dynamic orbit algorithm. First, a high-dimensional data matrix is constructed using the sliding window technique and the outliers in the high-dimensional data matrix are identified using Isolation Forest. Next, the hidden abnormal data within the time series are analyzed and repaired using the dynamic orbit algorithm. Then, the correlation analysis of the multivariate load and its weather data is carried out by the AR method and MIC method, and the high-dimensional feature matrix is constructed. Finally, the prediction values of the multi-load are generated based on the TCN-MMoL multi-task training network. Simulation analysis is conducted using the load data from a specific integrated energy system. The results demonstrate the proposed model’s ability to significantly improve load forecasting accuracy, thereby validating the correctness and effectiveness of this forecasting approach.

Adaptively optimal energy management for integrated hydrogen energy systems

AbstractIntegrated hydrogen energy systems (IHESs) have become attractive alternatives to cope with the depletion of fossil fuels and increasingly severe climate change problems. This paper proposes an adaptively optimal energy scheduling method based on deep deterministic policy gradient (DDPG) to improve the operational efficiency of IHES. The optimal scheduling problem is formulated as a Markov decision process problem with action space, environmental states, and action‐value function. The DDPG‐based optimal energy management algorithm with actor‐critic structure is proposed based on policy gradients and neural networks. Through actor‐critic network training and policy iteration, the energy management scheme can be adaptively optimized according to the dynamic responses of IHES. The benefits of the proposed algorithm are analysed through time‐domain simulations, and the scheduling robustness under different uncertain conditions is verified.

Optimal energy scheduling of the smart stand-alone electrical distribution grid considering power-to-gas storage technology and reserve strategy

Optimal energy scheduling of the smart stand-alone electrical distribution grid considering power-to-gas storage technology and reserve strategy

Optimal energy scheduling method for the North Cameroonian interconnected grid in response to load shedding

Renewable energy sources like sun and wind are intermittent, hence a hybrid system incorporating them is necessary. However, certain systems are more cost-effective and efficient than others, and they are not only more expensive but also far more harmful to the environment. To make up for the energy shortage in unconnected remote regions and urban areas with linked networks, renewable energies offer an alternative. This effort is focused on eliminating both load shedding and the pollution caused by conventional power plants that burn fossil fuels. In order to improve the interconnected Northern Cameroon grid, researchers looked into the possibility of using the permanently accessible sun and wind at the Waibé-Lokoro-Kalfou location in Cameroon. For the hot and humid climate of Waibé-Lokoro, Cameroon, based on four distributed generations, four combinations were established in the scenario. The goal was to maximize the net present value while minimising the energy expense. Electricity costs were found to drop from USD 0.097/kWh to USD 0.085/kWh under the PV-Wind-Grid-Battery scenario, saving a total of USD 0.54 million in net present cost. The particle swarm optimization method (PSO), genetic algorithmic algorithm (GA), cuckoo search approaches (CSA), as well as whale optimization algorithm (WOA) were utilized to calculate power losses as well as system size allocation. PSO was the only algorithm to converge quickly. The level of distortion caused by harmonics is measured experimentally to verify that power grid connectivity regulations are being followed. The standards for the IEEE 33-bus as well as IEEE 69-bus tests provide more precise voltage profiles for use in loss evaluation.Graphical

Optimal operation of smart energy hub considering high-temperature heat and power storage

Due to the impacts of high intermittent renewable resources, increasing their penetration in the generation sector of energy systems is still challenging. In this regard, the integration of different energy networks can play a key role in dealing with this challenge. This paper focuses on the analysis and optimization of multi-generation energy storage (MGES) system’s performance and investigates the role of this type of energy storage in the operation of future smart energy systems by considering 100% green energy goals. Hence, this paper first introduces high-temperature heat and power storage (HTHPS) system, as a novel MGES unit, for a local integrated energy system (IES) with different energy carriers. Then, a novel optimal energy scheduling scheme for this system is presented by considering the energy hub concept. The proposed IES is introduced as a Smart Energy Hub (SEH) which supplies local energy demands through local renewable resources generation, as well as upstream energy networks, by considering a competitive energy market. Finally, the performance of the proposed SEH is investigated by simulating different case studies and its effectiveness is proved in increasing the renewable generation penetration.

Optimal energy management and scheduling of a microgrid with integrated electric vehicles and cost minimization

Optimal energy management and scheduling of a microgrid with integrated electric vehicles and cost minimization

Optimizing Worst Case Data Freshness in RF-Powered Networked Embedded Systems

Maintaining real-time data freshness plays a critical role in ensuring system correctness and optimizing the system performance in networked embedded systems (NESs). To quantitatively measure the freshness of the collected real-time data, the concept of Age of Information (AoI) has been extensively studied in recent years. This article explores how to minimize the worst-case AoI of real-time data in RF-powered NESs. In such systems, one hybrid access point (HAP) transfers wireless power to a set of distributed sensor nodes, and in the meantime, receives the information from these sensor nodes. We utilize the metric of AoI to measure the data freshness and present a comprehensive analysis of the worst-case AoI of the real-time data in the target system. Based on the analysis, an optimal energy schedule solution is designed to judiciously determine individual sensor nodes’ energy and time allocation to minimize the worst-case AoI. Considering the varying importance of different information and sensor nodes in the target system, we further propose the optimal time and energy allocation scheme for minimizing the weighted worst-case AoI. A multi-node RF-powered NES testbed is implemented to validate the functional correctness of our solutions. The results show that our solutions significantly outperform the state-of-the-art solutions, reducing the worst-case AoI and weighted worst-case AoI by 69.3% and 75.1% on average, respectively.

Optimal Energy Scheduling in Smart Buildings with Electric Vehicle and Demand Response

In a number of countries, smart microgrid (SMG) technology is being exploited in the energy system infrastructure along with other generators such as electric and storage systems. In the framework of SMGs, communication links between generation and demand sides have been established to optimise energy consumption by means of economic signals. The framework of energy management for Smart Home appliances is presented in the present paper, taking account of appliance and electric vehicle participation. The approach to the energy schduling at day-ahead is defined as three strategies, such as optimum use of existing appliances, optimal production of electricity resources and optimal discharge and charging for electrical vehicles. Reducing energy generation costs and minimizing emissions of air pollutants are key objectives. Two case studies with the aim of reducing costs and emissions while maximizing system flexibility are taken into account to demonstrate preference and viability of home energy scheduling.

Editorial: Future electricity system based on energy internet: energy storage system design, optimal scheduling, security, attack model and countermeasures

Editorial: Future electricity system based on energy internet: energy storage system design, optimal scheduling, security, attack model and countermeasures

Retracted: Multi-objective grasshopper optimization algorithm for optimal energy scheduling by considering heat as integrated demand response

Due to the challenges associated with grid management, it is crucial to develop energy management systems that consider optimal resource performance within an integrated system and enable control of power exchange with the grid. Simultaneously, addressing the demand for multiple energy sources is a critical step towards achieving an economically and efficiently integrated energy system (IES), which plays a pivotal role in promoting sustainable IES development. Building upon these considerations, this article proposes a two-objective optimization model that incorporates the concept of demand response and integrated energy systems, while considering comprehensive economic and energy efficiency benefits. To ensure realism, a range of linear and nonlinear constraints are employed. To address such a problem, a two-stage modeling and solution approach is proposed. Initially, flexible electrical and thermal loads are combined to establish a mechanism for effectively responding to system behavior. Subsequently, a multi-objective model is presented to enhance energy generation and transfer efficiency, taking into account both demand response and economic aspects. Given the distinct properties of these objectives, a multi-objective grasshopper optimization algorithm (GOA) is developed. The Pareto concept is utilized to enhance search performance, and fuzzy theory is employed to select the optimal solution from the set of solutions. Finally, the proposed model and algorithm are evaluated under various operating conditions on the studied system. The results demonstrate that this model effectively improves economic aspects, enhances efficiency, and reduces pollutant emissions to an acceptable level.

Real-time energy scheduling for home energy management systems with an energy storage system and electric vehicle based on a supervised-learning-based strategy

With rising energy costs and concerns about environmental sustainability, there is a growing need to deploy Home Energy Management Systems (HEMS) that can efficiently manage household energy consumption. This paper proposes a new supervised-learning-based strategy for optimal energy scheduling of an HEMS that considers the integration of energy storage systems (ESS) and electric vehicles (EVs). The proposed supervised-learning-based HEMS framework aims to optimize the energy costs of households by forecasting the energy demand and simultaneously scheduling the charging and discharging operations of ESS and EV. From the scenarios extracted from historical data, the HEMS optimization problem is solved using a mixed-integer linear programming (MILP) solver to collect the datasets on the optimal actions of the ESS and EV. Accordingly, a supervised learning method is used to learn the optimal actions of the MILP solver using deep neural networks (DNNs). Well-trained DNNs act as decision-making tools that are subsequently applied to predict near-optimal actions for ESS and EV based on real-time data. The effectiveness of the proposed method is demonstrated through simulation results and compared with deep reinforcement learning-based and forecasting-based methods. The results show that the proposed method can significantly reduce energy costs and improve the efficiency of ESS and EV operations. Overall, the proposed supervised-learning-based HEMS offers a practical and effective solution for residential energy management.

Multistep Multiagent Reinforcement Learning for Optimal Energy Schedule Strategy of Charging Stations in Smart Grid.

An efficient energy scheduling strategy of a charging station is crucial for stabilizing the electricity market and accommodating the charging demand of electric vehicles (EVs). Most of the existing studies on energy scheduling strategies fail to coordinate the process of energy purchasing and distribution and, thus, cannot balance the energy supply and demand. Besides, the existence of multiple charging stations in a complex scenario makes it difficult to develop a unified schedule strategy for different charging stations. In order to solve these problems, we propose a multiagent reinforcement learning (MARL) method to learn the optimal energy purchasing strategy and an online heuristic dispatching scheme to develop a energy distribution strategy in this article. Unlike the traditional scheduling methods, the two proposed strategies are coordinated with each other in both temporal and spatial dimensions to develop the unified energy scheduling strategy for charging stations. Specifically, the proposed MARL method combines the multiagent deep deterministic policy gradient (MADDPG) principles for learning purchasing strategy and a long short-term memory (LSTM) neural network for predicting the charging demand of EVs. Moreover, a multistep reward function is developed to accelerate the learning process. The proposed method is verified by comprehensive simulation experiments based on real data of the electricity market in Chicago. The experiment results show that the proposed method can achieve better performance than other state-of-the-art energy scheduling methods in the charging market in terms of the economic profits and users' satisfaction ratio.

Co-optimization of multiple virtual power plants considering electricity-heat-carbon trading: A Stackelberg game strategy

With the improvement of the electricity-heat-carbon trading mechanism, it has been a trend for multiple virtual power plants (MVPP) to participate in the market competition. Firstly, in order to mitigate the burden of environmental pollution and balance the conflicting market interests of MVPP, this paper proposes a Stackelberg game strategy for the operator and MVPP considering a ladder-type carbon price mechanism and a dual-compensated demand response (DR) mechanism, which consists of an optimal pricing strategy for the operator and an optimal energy scheduling strategy for each VPP. During the game process, the operator serves as the leader, guiding the energy transactions of MVPP through price optimization, while each VPP acts as the follower, optimizing the internal aggregated units and loads based on the price issued. To solve the Stackelberg game equilibrium, a Kriging metamodel is then adopted to fit the energy scheduling model of the VPP. Finally, several cases are analyzed to verify that the proposed strategy can effectively contribute to the energy complementation and carbon reduction of MVPP.