Energy Management Model Research Articles

Transactive energy management systems: Mathematical models and formulations

AbstractRestructuring the power system with higher penetration of distributed energy resources (DERs) and intelligent devices offers the potential for more efficient, reliable, and better resource utilisation of power systems through the transactive energy framework (TEF). This article provides a general overview of the mathematical models and formulations of the TEF reported in the literature. TEF concepts can be applied to various levels of the power system. Here, the TEF‐related literature is divided into individual DER‐, building‐, microgrid‐, and macrogrid‐level TEF. The mathematical models of transactive agents corresponding to each level and power system network models are presented. Furthermore, TEF models for energy management and trading of integrated multi‐energy systems are analysed. Finally, the potential challenges and future research directions for transactive energy are discussed.

Optimal planning and design of integrated energy systems in a microgrid incorporating electric vehicles and fuel cell system

In recent decades, as a result of diminishing fossil fuel reserves and rising social concern, electric power systems as one of the largest sectors for environmental emissions, have had no choice but to search for alternatives to alleviate the environmental problems due to conventional generation system. Plug-in electric vehicles (PEVs) can cause difficulties in the electrical grid and system operation. To circumvent this issue, an efficacious stochastic optimization model is developed in this paper to enable the control entity to manage generation and storage assets by controlling the charging behavior of PEVs. To achieve the lowest total cost, a new strategy for reducing reliability costs is proposed. In this regard, vehicle-to-grid (V2G) tool is used to reduce the overall system cost. The presented energy management model for the MG in the grid-connected mode takes into consideration the uncertainty of output power of the wind turbine (WT) and the photovoltaic (PV), as well as the PEVs’ charging and discharging. In this study, an innovative and effective optimization algorithm known as the modified fluid search optimization algorithm (MFSO) is utilized to optimize the MG operation problem. The results show that the suggested model incorporating electric vehicles (EVs) can enable MG operation at the minimum cost and reliability. As can be observed, the amount of time required by the presented method to solve the first case problem is 6.64s, which is significantly less than the amount of time required by the other methods. In a similar vein, the MFSO method is noticeably quicker than both GA and PSO when applied to case studies 2 and 3.

An Optimal Method of Energy Management for Regional Energy System with a Shared Energy Storage

The regional energy system (RES) is a system that consumes multiple forms of energy in the region and achieves coordinated and efficient utilization of energy resources. The RES is composed of multiple micro energy systems (MESs); however, due to the mismatch of energy resources and different energy consumption within each MES, a large amount of clean energy is wasted, and each MES has to acquire extra energy. This significantly increases operation costs and contributes to environmental pollution. One of the promising ways to solve this problem is to deploy an energy storage system in the RES, which can make use of its advantages to transfer energy in space-time and fulfill the demand for loads in different periods, and conduct unified energy management for each MES in the RES. Nevertheless, a large number of users are deterred by the high investment in energy storage devices. A shared energy storage system (SESS) can allow multi-MESs to share one energy storage system, and meet the energy storage needs of different systems, to reduce the capital investment of energy storage systems and realize efficient consumption of clean energy. Taking multiple MESs as the object, this paper proposes a model and collaborative optimal strategy of energy management for the RES to accomplish high utilization of clean energy, environmental friendliness, and economy. First, the paper analyzes the internal energy supply characteristics of the RES and develops a model of the RES with an SESS. Then, the paper poses the management concept of load integration and unified energy distribution by using the operational information of each subsystem. An optimal operation strategy is established to minimize daily operation costs and achieve economic, environmentally friendly, and efficient operation of the RES. Third, by setting up scenarios such as no energy storage system and an independent energy storage system (IESS) of each MES and SESS, a case of a science and education park in Guangzhou, China, is illustrated for experiments. Numerical experiment results show that with an SESS built by the investor in the RES and applying the mentioned energy management strategy, the utilization of clean energy can be 100%, the operation costs can be reduced by up to 9.78%, the pollutant emission can be reduced by 3.92%, and the peak-to-valley difference can be decreased by 20.03%. Finally, the influence of energy storage service fees and electricity tariffs on daily operation costs is discussed, and the operation suggestions of the SESS are proposed. It validates the effectiveness of the proposed strategy.

Network-aware energy management for microgrids in distribution market: A leader-followers approach

With the increasing penetration of distributed energy resources (DERs) into distribution systems, microgrids can participate in the market clearing of distribution-level electricity market. This paper proposes a leader-followers-type bi-level model for energy management and market clearing of distribution system with microgrids based on Stackelberg game. Distribution system operator (DSO) is considered as the leader, and microgrid operators (MOs) as followers. In the proposed model, market clearing problem of distribution system is modeled in the upper level, while the energy management problem of microgrids is modeled in the lower level. The distribution locational marginal price (DLMP) is obtained in the upper level model and then transmitted to the lower level model as price signals for microgrids. The model is then reformulated as a Mathematical Program with Equilibrium Constraints (MPEC) problem and the stochastic MPEC model is constructed with the uncertainty of PVs in microgrids. Existence of equilibrium of the proposed single leader multi-follower game is analyzed. Numerical results demonstrate the effectiveness of the proposed model.

Factories of the future in the automotive industry of Russia: the balance between energy efficiency and digital development

The paper studies the prospects for the introduction of advanced production technologies (APT) and new business models at the enterprises of the automotive industry of the Russian Federation in order to find the balance between the investments into the more efficient energy management of the industrial buildings and the financing of digitalization process in the model of “factories of the future”. It includes the study of the opportunities that appear in companies that are “factories of the future”, the analysis of the existing experience in the implementation of APT and energy efficiency tools at the enterprises of the automotive industry in Russia and Europe and the identification of the factors limiting widespread “factories of the future” at the moment. The authors present an economic view on the problem of integrating both advanced production technologies and new energy management models into the activities of the Russian automotive industry enterprises.

Energy Management of PV-based Parking Lots Considering Utility Satisfaction

The urgent need to reduce greenhouse gas emissions has prompted the exploration of cleaner energy options such as renewable energy resources (RES) and electric vehicles (EVs). With the increasing use of RES and EVs, there are new opportunities and challenges for the stakeholders involved, and traditional centralized power system management methods are insufficient to handle the complexity. The energy management of photovoltaic (PV)-based parking lots is one of the challenges tackled in recent research. The main obstacle in applying previous research is the elimination of the utility objectives from the energy management models. This paper proposes an energy management framework that enables the integration of PV-based parking lots in a smart grid environment, considering the satisfaction of the utilities as a key player. The proposed framework is modeled using an optimization software package (GAMS), and the results are compared with two different cases to ensure its feasibility and effectiveness. The proposed model succeeded in taking the utilities satisfaction into account by managing the charging and discharging of the EVs.

An Energy Management System for the Optimal Operation of BESS in DC Microgrids: A Robust Convex Programming Approach

This paper uses a convex reformulation to deal with the robust, optimal energy management of battery energy storage systems (BESS) and renewable energies in DC microgrids. This relaxed mathematical model guarantees the global optimum regarding energy management problems, even when including uncertainties in demand and renewable energy. The proposed robust model can reach the best scheduling for energy management in worst-case cost scenarios while satisfying all requirements. Furthermore, a model for power transfer losses in converter devices is added to the proposed model by using a binary polynomial representation, which is convexified. This convexification is performed by transforming the nonlinear non-convex equations of the optimal energy management model into second-order cone constraints. Four scenarios implemented in the modified IEEE 123-bus radial distribution feeder are proposed in order to analyze the robust optimal energy management strategy: the deterministic model, demand uncertainty, uncertainty in solar and wind generators, and uncertainty in demand and solar and wind generators. In all scenarios, the energy management model reduces the total energy costs. Simulation scenario 1 showed daily operating costs of about US$ 4376.82, while simulation scenarios 2, 3, and 4, including demand and generation uncertainties, increased the daily operating costs by about US$ 5243.23 (19.79%), US$ 5072.23 (15.88%), and US$ 5738.75 (31.12%), respectively. Scenario 4 showed the highest costs, as it involves more uncertainty. Hence, the robust optimal energy management strategy dispatches more energy from conventional generators, increasing the operating costs to satisfy those of the worst case.

Energy management and operational planning of renewable energy resources-based microgrid with energy saving

• Proposing an environmental/economic model for optimal energy management. • Considering renewable energy resources in the presence of Photovoltaic and battery. • Presenting Hybrid Whale and Pattern Search Algorithm for optimizing Micro-grid. • Reporting superior solutions in Short-Term Scheduling and Micro-grid Energy Management. Energy resource scheduling is one of the major problems in power systems. This issue has become even more significant as renewable sources with intermittent power output are becoming more and more prevalent. Due to their low environmental impact and low operating costs, renewable energy sources (RESs) have attracted interest. The excess power generated by such generation methods may be a negative that affects power systems, hence issues relating to such systems should be properly handled. The optimal operation of a microgrid (MG) with several distributed generation (DG) units and uncertain behavior of RESs is suggested in this research using a stochastic optimization approach. So, for an MG fitted with a solar photovoltaic (PV) unit, this research proposes a day-ahead scheduling paradigm. In this regard, the effects of various climatic circumstances on the power production of the PV unit and the optimal scheduling of the MG have been examined in this research. In order to achieve this, statistics on solar irradiance were extracted from four distinct days from each of the four seasons. The single-objective optimization framework used to design the scheduling problem states that the objective function should be to minimize the total operating cost across the scheduling period. The aforementioned day-ahead scheduling problem can be solved by the "hybrid whale optimization algorithm and pattern search (HWOA-PS)” optimization algorithm, while both renewable and nonrenewable generating units, as well as an energy storage system are present. In order to confirm the higher performance of the recommended approach, a thorough comparison between the Hybrid WOA-PS algorithm and a few well-known optimization algorithms has also been conducted.

An overview of AC and DC microgrid energy management systems

<abstract> <p>In 2022, the global electricity consumption was 4,027 billion kWh, steadily increasing over the previous fifty years. Microgrids are required to integrate distributed energy sources (DES) into the utility power grid. They support renewable and nonrenewable distributed generation technologies and provide alternating current (AC) and direct current (DC) power through separate power connections. This paper presents a unified energy management system (EMS) paradigm with protection and control mechanisms, reactive power compensation, and frequency regulation for AC/DC microgrids. Microgrids link local loads to geographically dispersed power sources, allowing them to operate with or without the utility grid. Between 2021 and 2028, the expansion of the world's leading manufacturers will be driven by their commitment to technological advancements, infrastructure improvements, and a stable and secure global power supply. This article discusses iterative, linear, mixed integer linear, stochastic, and predictive microgrid EMS programming techniques. Iterative algorithms minimize the footprints of standalone systems, whereas linear programming optimizes energy management in freestanding hybrid systems with photovoltaic (PV). Mixed-integers linear programming (MILP) is useful for energy management modeling. Management of microgrid energy employs stochastic and robust optimization. Control and predictive modeling (MPC) generates energy management plans for microgrids. Future microgrids may use several AC/DC voltage standards to reduce power conversion stages and improve efficiency. Research into EMS interaction may be intriguing.</p> </abstract>

Predictive Energy Management in Internet of Things: Optimization of Smart Buildings for Energy Efficiency

As energy efficiency and sustainability become paramount in the face of growing urbanization and environmental concerns, predictive energy management in smart buildings has emerged as a promising avenue for mitigating energy consumption and optimizing resource utilization. In this paper, we investigate the application of advanced machine learning techniques, particularly a multi-layer Long Short-Term Memory (LSTM) model, within the framework of the Internet of Things (IoT), to predict and manage energy consumption. We rigorously evaluate our approach against a suite of machine learning baselines, including Linear Regression, Random Forest, Support Vector Machine, and Gradient Boosting, utilizing a comprehensive dataset encompassing power consumption data from smart home appliances and associated weather variables. Our experimental results demonstrate the superior predictive capabilities of the LSTM model, showcasing its ability to outperform traditional machine learning baselines across various metrics, including Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). These findings underscore the potential of deep learning models in capturing intricate temporal dependencies within energy consumption data, contributing to improved energy efficiency, cost savings, and environmental sustainability in smart building environments. The integration of predictive energy management models into IoT-enabled smart buildings holds the promise of a more intelligent and sustainable future in urban development and resource management.

A Q-learning based optimization method of energy management for peak load control of residential areas with CCHP systems

• An energy optimization management model for adaptive control of cooling, heating and electric peak load in coastal residential areas is presented. • The charging power of electric vehicle and the controllable power of air conditioner, water heater and refrigerator are coordinated for peak load control. • The coupling relationship and storage coordination of cooling, heating and electric energy is used to enhance the optimal control of peak load. • The deep Q-learning algorithm is used to solve the energy management optimization problem of coastal residential area for peak load control with good results. Aiming at the coastal residential area, considering the natural characteristics of wind and photovoltaic energy, the supply characteristics of gas and electric energy, the energy consumption law of cold, heat and electricity and the complementary and coupling relationship of various energy sources and load, this paper studies the formation conditions and various scenarios of peak load in the residential area with CCHP systems. Using the regulation characteristics of controllable loads such as electric vehicle, air conditioner, water heater and refrigerator, considering the coordination of cooling, heating and electric energy storage, the interchangeability and controllability of air conditioner based on electric/gas energy, and the transferability, interchangeability and controllability of water heater based on electric/gas/solar/heat energy, A collaborative energy management model for coastal resident area with electricity, gas, wind and solar energy is presented for peak load adaptive control of cooling, heating and electric energy. The Q-learning algorithm is used to solve the energy management optimization problem of peak load control in coastal residential areas, and good results are achieved, such as effectively reducing load fluctuations, improving the economy of the system, and increasing the self-sufficiency of the system.

A Framework for Several Electricity Retailers Cooperatively Implement Demand Response to Distributed Data Center

A distributed data center cluster (DDCC) consists of data centers that are geographically dispersed while managed by the same company. A DDCC is composed of several data centers in different regions, so it purchases electricity from several electricity retailers in different regions. Commonly, there is a lack of cooperation between the several electricity retailers. In this paper, a cooperation framework where several electricity retailers cooperatively implement incentive-based demand response (DR) to the same DDCC is proposed. Firstly, several electricity retailers which power for the same DDCC together publish their DR instructions to the DDCC. The DR instructions are analyzed in this paper. Then a novel DDCC energy management model is proposed for the DDCC to participate in the collaborative DR. Finally, a reasonable profit distribution mechanism is adopted for the several electricity retailers and the DDCC to allocate the entire profit of the collaborative DR. The case study verifies that the proposed cooperation framework can maximize the whole benefit of the DDCC and the several electricity retailers meanwhile ensures that the profits obtained by each member (the DDCC and the several electricity retailers) from the collaborative DR are not fewer than that of the independent operation.

Data-Driven Risk-Adjusted Robust Energy Management for Microgrids Integrating Demand Response Aggregator and Renewable Energies

Microgrids, integrating user-side demand response and zero marginal cost renewable energies, are potential components for future smart grids to reduce carbon emissions and improve power system resilience. In this paper, a day-ahead microgrid energy management framework with demand response aggregator as an intermediate coordinator is developed. The corresponding scheduling strategy is obtained to maximize the social welfare of the microgrid system, with considering the privacy of end-users and the uncertainty of renewable energies. To this end, firstly, a accelerated distributed optimization method based on Alternating Direction Method of Multipliers, named as FAST-PP-ADMM, is developed to protect the end-users privacy and improve the scalability of the microgrid system. Secondly, a data-driven risk-adjusted uncertain set is constructed with a distributionally robust chance-constraints model to characterize the forecast error of renewable energies. Based on the constructed uncertain set, a two-stage robust microgrid-side energy management model is solved by using the column-and-constraint generation (C&CG) method. Finally, the effectiveness of the proposed energy management framework and scheduling strategy is verified by simulations.

Energy Management Strategy for Seaport Integrated Energy System under Polymorphic Network

This paper studies the energy management problem of a seaport integrated energy system under the polymorphic network. Firstly, with the diversity of energy devices, a seaport integrated energy system based on the polymorphic network is established to ensure information exchange and energy interaction between heterogeneous devices, including the service layer, control layer, and data layer. Secondly, by analyzing the characteristics of different loads and the energy conversion hub, such as the power to gas (P2G) and combined cooling heating and power (CCHP), the energy management model for the seaport integrated energy system is constructed. Finally, we obtain the optimal solution by mixed integer linear programming, and the proposed strategy is used to a seaport integrated energy system including CCHP, P2G, clean energy and energy storage device. By comparing four different cases, the simulation results show a reduction in the cost of energy purchase and carbon emissions when applying our strategy with various device types and device failures. Moreover, considering the application of the proposed energy management strategy under seasonal variations, the optimal solution for the energy management problem of the seaport integrated energy system is obtained.

Multi-Objective Operation Strategy for a Community with RESs, Fuel Cell EVs and Hydrogen Energy System Considering Demand Response

Nowadays, it can be said that loads in the residential and transportation sector constitute an important part of global energy consumption. In this respect, due to environmental problems and in terms of ensuring energy reliability, it is seen that the energy demand is moving towards supplying it from on-site, renewable energy sources (RESs) and in a small-scale region, namely community. In this context, in this study, a multi-objective stochastic optimum energy management model for a renewable-supported hydrogen-based community is proposed with mixed-integer linear programming method. In the structure, the hydrogen energy system (HES) consisting of an electrolyzer, hydrogen tank, and fuel cell provides energy to the community and fuel support for hydrogen fuel cell electric vehicles (HFCEVs). In addition, a demand response (DR) program is implemented, where the room temperatures of the houses are remotely controlled. While electricity can be purchased from the power grid, hydrogen can be traded with the main hydrogen gas network. Besides, renewable spillage and carbon emission costs are considered. Various case studies are conducted using real data from Spain to demonstrate the effectiveness of the presented model. According to the results obtained, the proposed method can reduce the cost of the community by 58.67%.

Distributed peer-to-peer transactive residential energy management with cloud energy storage

Transactive energy management stimulates residential prosumers to participate in a local market. This improves energy efficiency and reduces energy costs. An effective method to reduce consumers' electricity bills is the usage of energy storage devices. However, the high investment and maintenance costs of these devices still limit their applications in the individual distributed framework. Recently, cloud energy storage (CES) as a shared energy storage technology has been introduced to provide storage services for residential consumers at a lower cost. In order to overcome the limitations of the individual framework and create new economic prospects, the CES is used in this paper to support numerous residential consumers in the energy market. This paper proposes an optimal energy management model in a transactive energy framework based on a distributed optimization mechanism for peer-to-peer (P2P) energy trading with the usage of the alternating direction method of multipliers. In this cooperative structure, residential users and CES participate in a decentralized framework to reach a consensus that preserves their privacy. In this proposed model, different types of loads, distributed energy resources and users' costs consisting of the grid energy cost, the P2P energy cost, the battery degradation cost, the thermal discomfort cost, the interruption cost, and the daily investment cost for individual batteries and CES are considered. The effectiveness of the proposed model is verified through various simulations. Numerical results show that the presence of CES in the proposed model and its participation in P2P energy trading reduces the cost of residential users, decreases the energy demand from the network, and increases CES owner's profit. Therefore, the application of the proposed model is valuable for the residential users, CES, and the grid.

Worst CVaR based energy management for generalized energy storage enabled building-integrated energy systems

Generalized energy storage (GES) can reduce the renewable energy curtailment, carbon emission and operational cost risk in the building-integrated energy system (BIES) energy management when faced with uncertainties of multi-energy loads and PV. In this context, GES integrated with the differentiated building thermal load-based virtual energy storage (VES) is proposed in the energy management model of BIES. Specifically, the differentiated building thermal load VES is introduced by differentiated personal factors (e.g., metabolic rate and clothing insulation) based on the predicted mean vote. Furthermore, a worst conditional value at risk (WCVaR) energy management method is developed to evaluate the operational cost risk of BIES. Meanwhile, possible probability distribution uncertainties of PV, electric and thermal loads are all fully considered by a boxlike uncertainty set. Then, the proposed WCVaR model is transformed into a linearized model by the Lagrange duality theory. Finally, the whole energy management model is solved by the CPLEX solver. Case studies and comparative analysis are performed based on a representative BIES. Numerical results show that the proposed model can reduce 33.55% of operational cost, 74.25% of carbon trading cost, and 16.96% of PV curtailment cost for BIES compared with that without GES. In addition, the conditional value at risk (CVaR) with the proposed WCVaR method ($5700.22) is between that with the traditional CVaR method considering multiple operational scenarios ($5684.68) and that with the traditional CVaR method only including the worst operational scenario ($6043.48), which indicates that the proposed WCVaR method can avoid overly-high and overly-low operational cost risks.

Data-driven worst conditional value at risk energy management model of energy station

The extensive integration of photovoltaics (PVs) leads to great uncertainties in the energy management of energy station (ES). In this context, this paper proposes a novel data-driven worst conditional value at risk (DWCVaR) energy management model for evaluating the operational risk brought by the uncertainty of PV outputs. Firstly, the wait-and-see energy management model is formulated to minimize the daily operational cost of ES. Then, the objective function is reformulated by the worst conditional value at risk (WCVaR) with the discrete probability distribution (PD). Furthermore, unlike the traditional WCVaR method with a box-like uncertainty set, the PD uncertainty set of PV outputs is described by the integrated norm (e.g., 1-norm and ∞-norm) constraints to cover possible PDs of PV outputs. Specifically, the integrated norm uncertainty set enables the explicit expression of the impact of historical data amount on the uncertainty set. Meanwhile, confidence levels of the uncertainty set can be adjusted for different conservative requirements. After the linearization of norm constraints, a bi-level primal-only iterative algorithm is proposed to solve the “min-max” optimization model. Finally, the whole DWCVaR energy management model is solved by the CPLEX solver. Case studies and comparative analysis are performed based on a representative ES. Numerical results show that the proposed DWCVaR energy management model is capable of achieving a reasonable and acceptable solution, and it significantly outperforms other methods.

Optimal planning of hybrid energy storage systems in traction power supply system with railway power conditioner

AbstractThe railway power conditioner (RPC) is a promising technology to improve the regenerative braking energy (RBE) utilization and power quality of the traction power supply system (TPSS). The hybrid energy storage systems (HESS) play a key role in the economic operation of TPSS due to the high cost of the system. The capacity and power of HESS are the critical issues affecting the system performance and cost. Therefore, a hierarchical energy management model is proposed to optimize the HESS sizing and the scheduling of TPSS with RPC. At the master level, a nonlinear mathematical model is built to minimize the HESS cost, which includes capital cost, replacement cost, and recovery cost. At the slave level, considering voltage unbalance as a constraint, a mixed‐integer linear programming model is established to minimize the electricity cost of railway operation. In order to solve the hierarchical nonlinear programming problem, an improved particle Swarm optimization (PSO) algorithm with a GUROBI solver embedded is proposed. Finally, the results show that the 14.08% cost reduction is achieved, and the standard limit value of voltage unbalance is satisfied.

Co-Operative Optimization Framework for Energy Management Considering CVaR Assessment and Game Theory

In this paper, a bi-level energy management framework based on Conditional Value at Risk (CVaR) and game theory is presented in the context of different ownership of multiple microgrid systems (MMGS) and microgrid aggregators (MAs). The energy interaction between MMGS and MAs can be regarded as a master–slave game, where microgrid aggregators as the leaders set the differentiated tariff for each MG to maximize its benefits, and MMGS as the follower responds to the tariff decision specified by the leader through peer-to-peer (P2P) energy sharing. The P2P energy sharing of MMGS can be regarded as a co-operative game, employing asymmetric Nash bargaining theory to allocate the co-operative surplus. The Conditional Value at Risk model was used to characterize the expected losses by microgrid aggregators due to the uncertainties of renewable energy resources. The Karush–Kuhn–Tucker conditions, Big-M method, and strong duality theory were employed to transform the bi-level nonlinear model of energy management into a single-level mixed integer linear programming model. The simulation results show that when MGs adopt the P2P energy-sharing operation mode, the total operating cost of MMGS can be reduced by 7.82%. The simulation results show that the proposed co-operative optimization framework can make the multiple microgrid systems obtain extra benefits and improve the risk resistance of microgrid aggregators.