Energy Management Method Research Articles

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.

Machine Learning-Based Estimation of COP and Multi-Objective Optimization of Operation Strategy for Heat Source Considering Electricity Cost and On-Site Consumption of Renewable Energy

Air conditioning is a significant consumer of electricity in buildings, accounting for around 40% of the total consumption. While previous studies have focused on planning methods to minimize electricity costs, recent years have seen an increasing need for energy management methods that consider environmental performance, such as CO2 emissions, alongside economic efficiency. This study proposes a mechanism to support stakeholders’ decision-making by calculating Pareto solutions based on the multi-objective optimization of economic and environmental characteristics for entities that own renewable energy generation facilities. Unlike many existing studies that assume a specific equation for COP (Coefficient of Performance) estimation, this study adopts a nonparametric COP estimation method using machine learning, resulting in a more realistic and flexible modeling of the system. The study also presents a model for selecting an operation strategy that balances environmental and economic goals, incorporating a thermal storage facility to improve the renewable energy rate. Specifically, we proposed and compared methods for calculating solutions using only the GA (Genetic Algorithm) and a two-step optimization method combining a GA and gradient-based optimization method, confirming the superiority of the two-step optimization method. The case study unveiled unique operational profiles corresponding to cost-saving, renewable-energy, and balanced orientation points, suggesting the existence of specific strategies tailored to each orientation. The findings of this study can help stakeholders make more informed decisions regarding energy management in air conditioning systems, with benefits for both the environment and the bottom line.

Energy management method for microgrids based on improved Stackelberg game real-time pricing model

With the rapid development of microgrids with distributed generations (DGs) and energy storage system (ESS), it is important to study energy management methods to improve the operation economy of microgrids. However, there is currently a lack of research on microgrid’s energy management models including multi-party groups such as wind turbines, photovoltaics and ESS. This paper proposed an energy trading management method of microgrids based on Stackelberg game real-time pricing mechanism, which can solve the more complex optimization operation problem of microgrids. First, the rolling optimization was carried out to determine the charging and discharging behavior of ESS for maximizing the total benefit microgrid in the next few time slots. Further, a Stackelberg game real-time pricing model was built. The electricity prices of different entities in the microgrid in the next time slot were optimized by microgrid operator (MGO) to determine the load demand of each DG, preference parameter in the utility function of DGs was improved to promote the internal energy interaction and the economic benefits of the microgrid. Finally, the results show that our method can effectively improve DGs’ total utility and stimulate energy trading within the microgrid. Compared with no optimization and traditional method, the daily profit of MGO obtained by our method was increased by 31.89% and 5.4% respectively, verifying the economics of the proposed method.

Low-cost fuzzy logic-controlled home energy management system

This paper presents a low-cost method for real-time energy management in residences. Light, motion, temperature, and sound sensors are system inputs. Lighting, heating, and cooling output powers are set according to sensor data and consumer conditions. The system is controlled by using three different fuzzy logic inference engines together with a microcontroller, sensors, and Nextion HMI display. The lighting, cooling, and heating can be precisely controlled according to the conditions of the house. This ensures that energy consumption is minimized while maintaining an appropriate level of comfort for the users. This shows that the system is designed as user-friendly and can be operated easily by the consumer. Thus, whether the consumers are at home or not, the consumption of electricity, water, and natural gas is controlled, and unnecessary consumption is prevented. The results show that such systems can effectively reduce energy consumption while maintaining user comfort, and this system could be an essential component of home energy management systems.

STRATEGIES OF REGIONAL POWER COMPANIES IN THE TRANSFORMATION TOWARDS GREEN MEGAWATTS AND OVERCOMING THE ENERGY CRISIS IN MODERN CONDITIONS

High gas and electricity prices due to the all this crisis in energy, which has arisen globally, has created a more attractive effect for renewable energy sources. Although they were quite different in the previous planning period, currently the utilization of renewable energy sources is more pronounced through different strategies both in planning and on the market. This is especially pronounced in the case of the strongest industrial countries, which dictate that trend.global energy crisis this year have made renewable energy technologies (RES) more attractive. For this reason, as well as the increasing need for new energy sources, new strategies for managing both energy and renewable energy sources are necessary. Greater investment in new sources of energy will significantly affect the world economy, which will have a major impact on the economies of developing countries. New ways of managing energy resources, which are more favorable for the human environment, contribute to maximizing their utilization. Their contribution will be all the greater and more profitable if they are an indicator of development and new technologies. Kosovo with its strategic position at the moment has a great advantage, as well as good relations between neighbors and the support of international factors give significantly more diverse in a positive sense and the application of modern and more cost-effective methods for energy management. The conceptual framework of this work can provide and enable modern functionalization of all resources and lead to the operationalization of management, i.e. management of transition and crisis, which are the accompanying effect of modern indicators on the energy market. Therefore, all this can be the basis for further research and comparisons. Everything points to the fact that the concept|" how to ensure a sustainable future " one of the most important tasks of the new constellation of leaders in energy, because every new opportunity is actually a challenge. Energy transitions and their successful implementation through strategies towards green and sustainable energy solutions surely lead to energy security.

An ECMS Based on Model Prediction Control for Series Hybrid Electric Mine Trucks

This paper presents an equivalent consumption minimization strategy (ECMS) based on model predictive control for series hybrid electric mine trucks (SHE-MTs), the objective of which is to minimize fuel consumption. Two critical works are presented to achieve the goal. Firstly, to gain the real-time speed trajectory on-line, a speed prediction model is established by utilizing a recurrent neural network (RNN). Specifically, a hybrid optimization algorithm based on the genetic algorithm (GA) and the particle swarm optimization algorithm (PSOA) is used to enhance the prediction precision of the speed prediction model. Then, on this basis, an ECMS based on MPC (ECMS-MPC) is proposed. In this process, to improve the real-time and working condition adaptability of the ECMS-MPC, the power-optimal fuel consumption mapping model of the range extender is established, and the equivalent factor (EF) is real-time adjusted by means of the PSOA. Finally, taking a cement mining road as the research object, the proposed strategy is simulated with the collected actual vehicle data. The experimental results indicate that the prediction precision of the proposed speed prediction model is over 98%, realizing on-line speed prediction effectively. Furthermore, compared to the existing real-time EMSs, its fuel-saving rate had an increase of more than 13%. This indicates that the designed ECMS-MPC is able to offer a novel and effective method for the on-line energy management of the SHE-MTs.

Implementation of various control methods for the efficient energy management in hybrid microgrid system

A hybrid microgrid is an energy system composed of multiple power sources such as photovoltaic panels, wind turbines, fossil-fuel generators, converters, battery storage systems, and an energy management system that guarantees stability and balance of the entire system. According to the system operation and the meteorological conditions, the microgrid can be linked to the electrical grid or work as a standalone system. The battery storage system is the key element of the microgrid and is integrated to maintain the load demand and better energy management using various controllers. In this paper, we presented an overview of energy management and control of the hybrid microgrid by proposing the implementation of the most cited control methods such as artificial neural network, fuzzy logic, sliding mode controller, and proportional integral derivative. Further, we addressed the solar power maximization using maximum power point tracking based on a developed artificial neural controller and compared it with the other controllers. Finally, implementing the proposed four controllers for the developed hybrid microgrid offers to find the best control strategy with multiple aspects and implications. Matlab/Simulink software was used to design a hybrid microgrid with two buses, AC Bus and DC Bus, to supply the power and fulfill the investigated load demands. The simulation is carried out for complicated system operation scenarios with multiple changes in system variables such as weather conditions and load demand to test the robustness of each controller. The simulation results and comparison show the performance of each control method in terms of voltage control, frequency stabilization and energy saving.

Reinforcement Learning-Based Energy Trading and Management of Regional Interconnected Microgrids

In this paper, we present a Value-Decomposition Deep Deterministic Policy Gradients (V3DPG) based Reinforcement Learning (RL) method for energy trading and management of regional interconnected microgrids (MGs). In practice, the state of an MG is time-varying and the traded energy flows continuously, which is generally neglected in researches. To address this problem, an Actor-Critic framework is adopted. Each MG has to make energy trading decision based on local observation and has no access to any knowledge of other MGs. We bring in the idea of value-decomposition in the training process to ensure the generation of feasible cooperative policies while maintaining MGs’ privacy and autonomous decision-making ability. Furthermore, in light of the uncertainty and fluctuation of renewable energy generation and users’ demand, a recurrent neural network (RNN) with Burn-In initialization is combined with critic network to achieve implicit predictions. Meanwhile, we also take Energy Storage System (ESS) with operational constraints into consideration and deem it as a virtual market innovatively. Experiments have been carried out under real-world data to verify the merit of the proposed method, compared to existing RL-based works.

Aging Mitigation for Battery Energy Storage System in Electric Vehicles

Battery energy storage systems (BESS) have been extensively investigated to improve the efficiency, economy, and stability of modern power systems and electric vehicles (EVs). However, it is still challenging to widely deploy BESS in commercial and industrial applications due to the concerns of battery aging. This paper proposes an integrated battery life loss modeling and anti-aging energy management (IBLEM) method for improving the total economy of BESS in EVs. The quantification of BESS aging cost is realized by a multifactorial battery life loss quantification model established by capturing aging characteristics from cell acceleration aging tests.Meanwhile, a charging event analysis method is proposed to deploy the built life loss model in vehicle BESS management. Two BESS active anti-aging vehicle energy management models: vehicle to grid (V2G) scheduling and plug-in hybrid electric vehicle (PHEV) power distribution, are further designed, where the battery life loss quantification model is used to generate the aging cost feedback signals. The performance of the developed method is validated on a V2G peak-shaving simulation system and a hybrid electric vehicle. The work in this paper presents a practical solution to quantify and mitigate battery aging costs by optimizing energy management strategies and thus can further promote transportation electrification.

A novel bio-based phase change material of methyl palmitate and decanoic acid eutectic mixture: Thermodynamic modeling and thermal performance

One of the most sustainable methods of energy management is thermal energy storage using renewable phase change materials. Phase change materials (PCMs) with high latent heat can store thermal energy when available and release it effectively when required. In this study, the thermal properties of a set of binary mixtures of methyl palmitate (MP) and decanoic acid (DA) have been evaluated as a new bio-based and renewable PCM. Both phase change materials are obtained from vegetable oil and are biomass-based materials. The results show that the binary eutectic mixture consisting of 30% MP and 70% DA has the desired properties as a PCM for applying as thermal energy storage in the building materials. These features include the convenient melting and freezing temperatures (Tm = 20.19 °C, Tf = 15.08 °C) and the high value of corresponding latent heats (ΔHf =202.3 J.g−1, ΔHm = 201.43 J.g−1). Evaluating other thermo-physical properties of this mixture also confirms its fitness for the PCM role. The composition and melting point of the eutectic point and the solid-liquid phase equilibrium for these mixtures were predicted by several thermodynamic models from which UNIQUAC showed the lowest Average Relative Deviation percentage (ARD%) of 0.29%.

Optimal Energy Management for Virtual Power Plant Considering Operation and Degradation Costs of Energy Storage System and Generators

Even though generating electricity from Renewable Energy (RE) and electrification of transportation with Electric Vehicles (EVs) can reduce climate change impacts, uncertainties of the RE and charged demand of EVs are significant challenges for energy management in power systems. To deal with this problem, this paper proposes an optimal energy management method using the Virtual Power Plant (VPP) concept for the power system considering solar PhotoVoltaics (PVs) and Electric Vehicle Charging Stations (EVCS). The Differential Evolution (DE) algorithm is applied to manage energy in the power system to minimize the operation cost of generators and degradation costs in Energy Storage Systems (ESS) and generators. The effectiveness of the proposed approach is examined and tested on the IEEE 24 bus Reliability Test System (RTS 24) using the MATPOWER tool on the MATLAB program for calculating Optimal Power Flow (OPF). In this study, two situations before and after applying the proposed method are considered. The simulation results demonstrate that a branch constraint violation occurs before using optimal energy management using the VPP concept. In order to solve this issue, the DE algorithm for optimal energy management using the VPP concept is applied by dividing the studied case into two subcases as follows. For the first subcase, two objectives consisting of the minimization of the generator operation cost and the minimization of the battery degradation cost in ESS are considered. In the second case, three objectives comprising the two mentioned objectives with the minimization of generator degradation cost are considered. The results demonstrate that branch constraint violations can be avoided by applying optimal energy management using the VPP concept. This study also suggests considering the generator degradation cost in the objective function, which can minimize the total costs by 7.06% per day compared with the total cost of the first case.

Ascent adaptive energy management method for solid rocket‐powered hypersonic vehicle under uncertainties

SummaryThe solid rocket booster is widely used in the launch mission for hypersonic vehicles nowadays, whose ascent energy management is still a challenging problem due to the inherent dynamic nonlinearity. This paper casts the problem into an equivalent ascent guidance one and proposes a model predictive static programming (MPSP) based solution. The MPSP‐based method is a computationally efficient solver that adapts to different energy demands while guaranteeing the satisfaction of terminal constraints. To further improve the performance of MPSP in this problem, a Bézier spline guidance method is proposed to generate high‐quality reference trajectories such that the MPSP can converge with high accuracy while satisfying the real‐time requirement. Additionally, an online parameter estimation based on the extended Kalman filter is employed to avoid large terminal state deviations incurred by parametric uncertainties during the Bézier curve computation and linearization in MPSP. Numerical simulations with different hard terminal constraints and uncertainties are conducted to demonstrate the effectiveness and robustness of the proposed algorithm. The simulation results show that our proposed algorithm can guide the hypersonic vehicle to a given terminal state under uncertainties and outperforms several existing ascent energy management methods.

Blockchain for secure decentralized energy management of multi-energy system using state machine replication

Decentralized energy management can preserve the privacy of individual energy systems while mitigating computational and communication burdens. However, most decentralized energy management methods are partially decentralized and cannot ensure information exchange security. Therefore, this paper provides a secure fully decentralized energy management by using blockchain. First, a fully decentralized energy management framework using the optimality condition decomposition (OCD) is provided, in which individual energy system operators only exchange the boundary information with their peers rather than submitting proprietary information to a centralized system operator. Then, an asynchronous mechanism is proposed for updating the information exchange in OCD, enabling the proposed decentralized management to work under potential communication latency or interruption. Furthermore, the blockchain-based framework with state machine replication (SMR) based consensus algorithm is provided to safeguard the information exchange among individual energy systems in a secure and tamper-proof manner. The proposed decentralized energy management is tested on a multi-energy system with seven subsystems and a real-world multi-energy system in North China. The numerical results demonstrate the effectiveness of the proposed method in privacy protection and data security enhancement. The proposed method can prevent the cost increase caused by cheating activities, which in some subsystems can reach 17.6%. Additionally, the proposed fully decentralized method outperforms the partially decentralized method by 37.7% in reducing computation time. Also demonstrated are the computational precision, scalability and adaptability of the proposed method.1Information about the data used in the case study of this paper, including how to access them, can be found in the Cardiff University data catalogue at http://doi.org/10.17035/d.2023.0247331015.1

Improving the Efficiency of Renewable Energy Assets by Optimizing the Matching of Supply and Demand Using a Smart Battery Scheduling Algorithm

Given the fundamental role of renewable energy assets in achieving global temperature control targets, new energy management methods are required to efficiently match intermittent renewable generation and demand. Based on analysing various designed cases, this paper explores a number of heuristics for a smart battery scheduling algorithm that efficiently matches available power supply and demand. The core of improvement of the proposed smart battery scheduling algorithm is exploiting future knowledge, which can be realized by current state-of-the-art forecasting techniques, to effectively store and trade energy. The performance of the developed heuristic battery scheduling algorithm using forecast data of demands, generation, and energy prices is compared to a heuristic baseline algorithm, where decisions are made solely on the current state of the battery, demand, and generation. The battery scheduling algorithms are tested using real data from two large-scale smart energy trials in the UK, in addition to various types and levels of simulated uncertainty in forecasts. The results show that when using a battery to store generated energy, on average, the newly proposed algorithm outperforms the baseline algorithm, obtaining up to 20–60% more profit for the prosumer from their energy assets, in cases where the battery is optimally sized and high-quality forecasts are available. Crucially, the proposed algorithm generates greater profit than the baseline method even with large uncertainty on the forecast, showing the robustness of the proposed solution. On average, only 2–12% of profit is lost on generation and demand uncertainty compared to perfect forecasts. Furthermore, the performance of the proposed algorithm increases as the uncertainty decreases, showing great promise for the algorithm as the quality of forecasting keeps improving.

Two-stage energy management method of integrated energy system considering pre-transaction behavior of energy service provider and users

Energy management is an important mean for the integrated energy system (IES) to realize efficient energy utilization and low-carbon economic operation, and the proper pricing strategy of energy service provider (ESP) and energy consumption strategies of users are important factors in realizing efficient energy management of electric-thermal IES. This paper proposes a two-stage energy management method for IES considering the energy pre-transaction behavior of ESP and users. First, an energy interaction framework of the IES is proposed with the agents of the ESP and users. Subsequently, a two-stage energy management method that considers the pre-transaction behavior and operation optimization of the IES is established. In the first stage, the energy pre-transaction behavior model between ESP and users is studied, where the energy pre-transaction behavior is formulated as a discrete finite Markov decision process (MDP) to obtain the maximum price of ESP and the maximum adjustment load of users. In the second stage, considering the constraints of the energy price and load adjustment in the first stage, an energy interaction game model based on Stackelberg is established to obtain the optimal price and energy management strategy. Finally, the numerical results show that the proposed two-stage energy management method improves the profitability of ESP and users, promotes the utilization ratio of renewable energy and reduces the carbon emissions of the system. The numerical results show that the proposed method can significantly promote the profit of ESP by 58.47% and comprehensive benefit of users by 5.72%. In addition, the proposed method is also effective in improving the utilization of renewable energy and reducing carbon emissions, and the carbon emission of IES reduced 4.61% compared with traditional method.

Energy and power management system for microgrids of large‐scale building prosumers

AbstractA method for optimal energy and power management of microgrids consisting of mega buildings, plug‐in electric vehicles (PEVs) and renewable energy sources (RES) with low computation requirements is proposed by the authors. Thermal and electrical loads are considered for the operation scheduling of the microgrid. In case of non‐interconnected operation of the microgrid with the main power grid, the proposed method allows the microgrid to meet the power demand by the buildings and distribution loads exploiting only the hosted PEVs, the integrated RES and, if it is necessary or financially optimal, building auxiliary diesel generators. The primary goal of the suggested algorithm is to significantly reduce the overall daily cost of the microgrid's operation while simultaneously meeting a wide range of constraints. The implementation of the method is based on the exploitation of a two‐level hierarchical multi‐agent system (MAS) at the level of the microgrid. Suitably defined flexibilities of the microgrid's components to change their power are used to implement optimal power dispatch to them. Detailed simulation results indicated that a remarkable cost reduction of 27% can be achieved.

Retailer energy management of electric energy by combining demand response and hydrogen storage systems, renewable sources and electric vehicles

The increasing pollution caused by conventional cars and the problems caused by the use of fossil fuels have drawn the attention of researchers and manufacturers to the design of cars that use clean fuels. Electric vehicles connected to the network have a significant impact on reducing environmental pollution and transportation costs, especially in big cities. The cost of supplying loads to subscribers in the distribution network also includes generation and transmission costs. These costs are directly related to the intelligence of the distribution network and the total amount of energy of electric vehicles. The contribution of each generation unit and each transmission line must be calculated to determine the generation and transmission costs. In this research, in order to maximize the profit of the parking lot owner, improve voltage drop and load factor, a comprehensive framework for optimal energy management in a parking lot is presented, which can provide a method to control the charging of electric vehicles, in addition to meeting the needs of their owners, only as a series of controllable loads that they need to receive electrical energy to charge their batteries. In the next step, considering the inherent characteristic of electric cars, i.e. having a battery, and looking at them as a series of storage resources that can return the electric energy in their battery to the grid if necessary, a method to simultaneously control their charging and discharging is provided. In the final step of the paper, it is assumed that hydrogen storage systems will also enter the circuit, and thus, a comprehensive method for energy management is proposed. Finally, the linearized model of demand response and the proposed scheme along with the modeling of hydrogen storage and electric vehicles are considered to be part of contribution to improve the operation and economic situation of the network.

Decentralized Reinforcement Learning Approach for Microgrid Energy Management in Stochastic Environment

Microgrids are considered to be smart power grids that can integrate Distributed Energy Resources (DERs) in the main grid cleanly and reliably. Due to the random and unpredictable nature of Renewable Energy Sources (RESs) and electricity demand, designing a control system for microgrid energy management is a complex task. In addition, the policies of microgrid agents are changing over time to improve their expected profits. Therefore, the problem is stochastic and the policies of the agents are not stationary and deterministic. This paper proposes a fully decentralized multiagent Energy Management System (EMS) for microgrids using the reinforcement learning and stochastic game. The microgrid agents, comprising customers, and DERs are considered as intelligent and autonomous decision makers. The proposed method solves a distributed optimization problem for each self-interested decision maker. Interactions between the decision makers and the environment during the learning phase lead the system to converge to the optimal equilibrium point in which the benefits of all the agents are maximized. Simulation studies using a real dataset demonstrate the effectiveness of the proposed method for the hourly energy management of microgrids.

The Optimal Energy Management Methods of The Hybrid Power System

The hybrid renewable energy system is a particular type of energy system which can be used to reduce grid losses and improve efficiency. In general, during the design step, there are two important conditions: the first is the availability, and the second is the cost of equipment. This paper considers these restrictions and uses hybrid renewable energy sources (HRES). The problems of traditional energy are many and varied, and the percentage of the pollution that comes from it is also many, so people began to search for renewable energy sources with problems, pollution, and lower cost. Therefore, several studies have been conducted on integrating different types of renewable energies to generate electricity and meet the consumer’s needs. This paper provides a comprehensive overview of the recent methods used to Improve and manage hybrid energy systems.

Eco-Environmental Uncertain Management of Various Energy Hubs within a Microgrid Concentrated on Demand Response, Intelligent Parking Lots, and Power-to-Gas Unit Effects

Nowadays, consumption of different energy carriers is increasing due to the division of community activities in various sectors such as residential, commercial, and industrial. The energy hub concept is used to meet the demands of different energy carriers in these sections. In this paper, a new method for energy management of a microgrid is presented in an intelligent network based on three types of commercial, residential, and industrial energy hubs. In the energy hubs, a wide range of components, including renewable and nonrenewable generation units, converters, storage devices, intelligent parking lots consisting of electric vehicles, P2G units, and cogeneration units, are used to supply electrical, heating, cooling, and natural gas energies. Some parameters like renewable generations, energy demands, and arriving and departure time of electric vehicles are considered to be uncertain, and a relevant method is applied to provide a near-realistic profile for them. In addition, a demand response paradigm has also been proposed for three types of electrical, heating, and cooling demands at the hub output side. Therefore, in this method, a mixed integer linear programming model is proposed based on benefit and reduction of emission caused by the activity of gas-burning units for short-term planning and obtaining an optimal solution for generation and sending loads in a distribution network containing energy hubs. To evaluate the performance of the proposed modeling and structure, the presented approach is applied on a modified 33-bus IEEE test network. According to the results of the energy management model, it is possible to significantly increase benefits and also obtain a smoother consumption pattern in consumption with financial incentives.