Optimal Energy Management Model Research Articles

Physical model-assisted deep reinforcement learning for energy management optimization of industrial electric-hydrogen coupling system with hybrid energy storage

Utilizing renewable energy sources (RESs), such as wind and solar, to convert electrical energy into hydrogen energy for industrial users with different types of energy storage can enhance the integration of green electricity, thereby replacing fossil fuels like natural gas. In the energy management optimization of industrial electric‑hydrogen coupling system (EHCS) with high RESs integration, the traditional optimization methods cannot effectively address the nonlinear characteristics of equipment and stochastic RES generation, while the model-free reinforcement learning methods struggle to improve the training efficiency and may lead to constraint violation. To address this challenge, this paper proposes a short time scale energy management approach for EHCS based on physical models assisted deep reinforcement learning (DRL). Firstly, the energy management optimization model of the EHCS is developed based on the operation characteristics of the equipment within the system. Secondly, the energy management model of EHCS is formulated as a DRL framework. The DRL agent is efficiently trained with the assistance of the action transformation which takes into account the physical characteristics of the equipment during operation. Finally, a case study is conducted to verify the effectiveness of the proposed model for achieving high-efficiency agent training, reducing decision-making time for equipment scheduling, addressing the stochastic RES generation, and realizing the economic operation of EHCS with good performance based on the equipment characteristics.

Optimal scheduling of a microgrid based on renewable resources and demand response program using stochastic and IGDT-based approach

Optimal economic scheduling of microgrids with photovoltaic (PV) and wind generation has gained increased attention during recent years. Integration of renewable energy resources in microgrids requires increasingly active control and management of energy storages and demand response (DR). In this paper, a risk-based stochastic optimal energy management model is developed for microgrid with renewables, energy storage and load control by time-of-use-based DR programs. Microgrid includes PV system, wind system, micro-turbine, fuel cell, electric vehicle (EV), and energy storage. Information-gap decision theory (IGDT) is employed to address the uncertainty of loads and to provide the operating strategies for the microgrid controllable energy resources. This proposed model has been solved as a mixed-integer non-linear programming (MINLP) in General Algebraic Modeling System (GAMS) software and simulation results in different conditions are studied and discussed. Three different risk management strategies have been studied such as risk-averse, risk-neutral and risk-seeker mode. The simulation results indicate that the impacts of risk-averseness or risk-seeker of the decision maker affect the system operation. For instance, the results showed the DR program's role in risk-averse and risk-taking strategies, impacting consumption and costs. The proposed model ensures the risk-averse decision-maker that if the uncertain parameter deviates within the optimum robustness region, the final cost will not exceed the critical cost. On the other hand, the risk-seeking decision-maker can reach lower final costs by accepting the risks if the uncertain parameter deviates favorably within the opportunity region. Decision-makers can manage risks by adjusting consumption. Thus, considering the cost of risk management is crucial, as it increases with robust or opportunistic approaches.

Presenting an optimal energy management model in microgrids using APSO algorithm

Presenting an optimal energy management model in microgrids using APSO algorithm

Intelligent energy management for an on-grid hydrogen refueling station based on dueling double deep Q network algorithm with NoisyNet

The development of hydrogen-based vehicles (HVs) can help achieve a zero-carbon future; however, the availability of hydrogen refueling stations (HRSs) prevents their widespread adoption as personal vehicles. Existing studies have investigated the energy management of HRSs using various methods. However, there have been no reports of implementing a deep reinforcement learning (DRL) approach to address these uncertainties and achieve real-time decision making. This study proposes an energy management optimization model of an on-grid HRS based on the improved dueling double deep Q network(D3QN) algorithm with NoisyNet. The primary goal is to reduce the cost of operating an HRS and improve voltage stability while satisfying the hydrogen demand of HVs. Notably, this study adopts an improved version of the double deep Q network (DDQN), that is, the NoisyNet-D3QN (NN-D3QN) approach, because NoisyNet can aid efficient exploration and the dueling network can generalize learning across actions. The adopted NN-D3QN algorithm has better performance than other basic algorithms. Compared with the NN-DDQN, D3QN, and DDQN approaches, the reward of the proposed method increases by 19.08 %, 31.66 %, and 39.26 %, respectively.

False Data Injection Attack Against the Optimal Energy Management of Active Distribution Networks: A Bilevel Programming Model

With the wide integration of advanced measurement equipment and communication networks into active distribution networks (ADNs), the ADN cyber-physical system (CPS) is facing the risk of false data injection attacks (FDIAs). This paper proposes a bilevel attack model to obtain optimal false data, which maximizes power losses, photovoltaic (PV) curtailment, and load shedding while ensuring the concealment of the attack. Different from the FDIA for the transmission networks, the FDIA targeting ADNs not only injects false data into load demand but also further injects false data into PV output, achieving an attack through the cooperation of the two types of false data injection. Moreover, differing from a single-level attack model, the proposed bilevel model takes the optimal energy management model executed by the ADN system operators as the lower-level model, using a master-slave game to obtain the optimal false data that considers operator responses, making it more in line with practical engineering. Using the Karush-Kuhn-Tucker (KKT) conditions, the bilevel model is transformed into a single-level model for ease of solution. Simulation results based on the IEEE 33-bus test case confirm the effectiveness of the proposed model and reveal the risk of the ADN under FDIA.

An inertial neurodynamic algorithm for collaborative time-varying energy management for energy internet containing distributed energy resources

This paper investigates the energy management of an Energy Internet (EI) that integrates multiple energy networks. Based on the structure of EI and the fluctuation of load, an optimal energy management model considering part of the load as time-varying factor is proposed. Under the influence of load, the actual energy management problem (EMP) is formulated as a time-varying optimization problem subject to a set of global and local constraints. Its optimal solution varies continuously with time, which leads to difficulties in obtaining optimal solutions by conventional algorithms. Consequently, to obtain better tracking performance, this paper proposes a distributed inertial projection neurodynamics algorithm (DIPNA) based on the Nesterov accelerated gradient descent method. This algorithm tracks the optimal solution of the EMP with a fast convergence rate O1/t2, providing a referenceable active power for each energy unit in real time. Finally, the performance evaluation results demonstrated the effectiveness and fast convergence of the proposed algorithm.

Potential Analysis and Optimal Management of Winter Electric Heating in Rural China Based on V2H Technology

In order to improve the air pollution problem in northern China in winter, coal-to-electricity (CtE) projects are being vigorously implemented. Although the CtE project has a positive effect on alleviating air pollution and accelerating clean energy development, the economic benefits of electric heating are currently poor. In this study, a system based on vehicle-to-home (V2H) and photovoltaic power generation that can effectively improve the benefits of CtE projects is proposed. First, a V2H-based village microgrid is proposed. The winter temperature and direct radiation of the Beijing CtE project area are analyzed. Extreme operating conditions and typical operating conditions are constructed for potential analysis. After that, a bi-layer optimization model for energy management considering travel characteristics is proposed. The upper layer is a village-level microgrid energy-dispatching model considering meeting the heating load demand, and the lower layer is a multi-vehicle energy distribution model considering the battery degradation. The results show that the distribution grid expansion capacity of the electric heating system based on V2H and PV generation is reduced by 45.9%, and the residents’ electricity bills are reduced by 68.5%. The consumption of PV can be completed. This study has effectively increased the benefits of electric heating in northern China during winter. This helps the CtE project to be further promoted without leading to large subsidies from the government and the State Grid.

A model for optimal energy management in a microgrid using biogas

A model for optimal energy management in a microgrid using biogas

Research on Global Optimal Energy Management Strategy of Agricultural Hybrid Tractor Equipped with CVT

This paper presents a proposed global optimal energy management strategy based on dynamic programming to enhance the energy consumption efficiency of an agricultural hybrid tractor that is equipped with a continuously variable transmission (CVT). Firstly, using a diesel-electric parallel agricultural hybrid tractor as the research object, a tractor-rotary tillage coupling dynamics model is constructed. Secondly, with the torque and speed of the motor, the torque and speed of the diesel engine, and the CVT speed ratio as the control variables, the state of charge (SOC) of the power battery as the state variable, and the goal of minimizing the total energy consumption of the whole machine, a global optimal energy management model based on dynamic programming is established. Finally, the field operation measured data is injected into the MATLAB simulation model, and experiments are carried out to verify the effectiveness of the energy management strategy. The results show that compared with the power-following energy management strategy, the proposed energy management strategy can make the diesel engine and electric motor work in the optimal area, and effectively reduce the total cost of energy consumption of the tractor during field operations. Under the condition of rotary tillage, the total cost of energy consumption is decreased by 16.89%.

Integrated model for optimal energy management and demand response of microgrids considering hybrid hydrogen-battery storage systems

Hybrid hydrogen-battery (HHB) based energy storage technologies have recently become matters of significant interest to enable sustainable and net zero-emission microgrids characterized with high penetration levels of variable renewable energy sources (RES) such as solar and wind. In this regard, there are multiple objectives and constraints controlling the scheduled decision-making framework that manages these microgrid resources while supplying the demand. As such, this paper introduces an integrated energy management system (IEMS) to concurrently optimize the operation schedule of the HHB storage system and provide demand response (DR) via adequate shifting slots for the elastic loads of a microgrid setup. The proposed IEMS aims to optimally manage the microgrid’s energy by minimizing three conflicting objective functions: the electricity and battery degradation costs, customers’ discomfort, and peak-to-average ratio (PAR). The proposed IEMS is developed for microgrids containing a mix of variable RES, an HHB storage system, and a local power consumption considering dynamic grid tariff. The IEMS is formulated as a mixed-integer nonlinear problem and is solved using a multi-objective artificial hummingbird optimizer (MOAHA). To account for the variability and uncertainty of RES, the proposed IEMS is tested under four scenarios: good, bad, average weather scenarios, and a forecasted weather profile based on a stochastic model of RES. The performance of the proposed IEMS is evaluated via comprehensive comparative analyses with the conventional energy management strategy (CEMS) without and with consideration of the HHB storage system and DR. Moreover, the performance of MOAHA has been assessed versus the state-of-the-art multi-objective particle swarm optimizer (MOPSO). For the purpose of these comparisons, four quality metrics have been utilized to quantify the benefits of the proposed IEMS: renewable contribution factor (RCF), greenhouse gas (GHG) emission, loss of power supply probability (LPSP), and saving cost (SC). The results show that the proposed IEMS yields better-optimized values that help the microgrid operator identify the sweet spot of the conflicting objectives, where it has increased the customer saving to be (82.95%, 29.65%, 33.33%, and 33.00 %), (73%,23.94%, 25.49%, and 31.65%) and (10.69%,3.62%, 1.59%, and 1.67%) compared to the CEMS without/with including HHB. and IEMS based-MOPSO over the four studied scenarios, respectively. Also, the results show the superiority of the proposed IEMS in improving the identified four quality metrics compared to the CEMS without/with considering HHB storage systems and IEMS-based-MOPSO. Accordingly, integrating HHB and implementing optimized IEMS based-MOAHA with considering the DR approach plays a vital role in providing a profit for the customer, protecting the environment from emissions, and enhancing the system’s reliability with considering the weather uncertainty.

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 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.

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.

Impact of decentralized microgrids optimal energy management on power system dynamics

This paper proposes a unified framework between the optimal energy management (OEM) problem of grid-connected microgrids and the frequency control of power systems through time-domain simulations. The proposed formulation is aimed at analyzing how different objectives for the microgrid OEM and control strategies affect the system frequency regulation. Each OEM model minimizes the microgrid local objective while ensuring energy reserves and operational limits by solving a two-stage stochastic optimization mathematical model. Two control approaches of microgrids are considered, namely, greedy and cooperative. The proposed framework is evaluated considering the IEEE 39-bus test system, including multiple microgrids with different energy storage capacities, and control strategies. Results show an improvement in system’s frequency deviation when MGs operate in the cooperative configuration while simultaneously reducing their operational costs.

Enhancing dynamic energy network management using a multiagent cloud-fog structure

Smart Grid benefits from information and communications technology (ICT) to integrate data from different sources across the network. The growing market for electric vehicles (EV) and electric devices is increasing energy demand. To manage a large amount of complex intelligent equipment, EV charging and discharging, and data-intensive devices, a reliable modern smart grid with a service-oriented, secure, efficient, and cost-effective system is compelling. Cloud computing is a promising approach to achieve that objective as monitoring power grid data flow and data center management are the key. This paper proposes an optimization model for energy management within the power cloud. Our energy system model enables accessible services to computing resources and real-time data stream processing within an integrated environment. Using digital technology, the proper implementation of our model reduces costs and energy consumption and improves the smart grid reliability for customers and energy providers in a distributed manner. This paper presents the implementation of a three-procedure optimization algorithm within a novel Multi-Agent Cloud-fog Structure (MACS) to meet the requirements raised by smart grid communication and distribution. Our model promotes reliable energy consumption adjustments by end-users who can choose power supplied by solar, wind, geothermal, biomass, or other renewable sources or from non-renewable sources in ways that reveal opportunities for demand-supply balance and energy saving.

Research on Energy Management of a Virtual Power Plant Based on the Improved Cooperative Particle Swarm Optimization Algorithm

Energy management of virtual power plants (VPPs) directly affects operators’ operating profits and is also related to users’ comfort and economy. In order to provide a reasonable scheme for scheduling each unit of the VPP and to improve the operating profits of the VPP, this study focuses on the optimization of VPP energy management under the premise of ensuring the comfort of flexible load users. First, flexible loads are divided into time-shiftable load (TL), power-variable load (PL), and interruptible load (IL), and their accurate power consumption models are established, respectively. Then, aiming at maximizing the operation profits of a VPP operator, an optimization model of VPP energy management considering user comfort is proposed. Finally, the improved cooperative particle swarm optimization (ICPSO) algorithm is applied to solve the proposed VPP energy management optimization model, and the optimal scheduling scheme of VPP energy management is obtained. Taking a VPP in the coastal area of China as an example, results show that the optimization model proposed in this article has the advantages of good economy and higher user comfort. Meanwhile, the ICPSO algorithm has the characteristics of faster optimization speed and higher accuracy when solving the problem with multiple variables.

Learning based cost optimal energy management model for campus microgrid systems

The introduction of microgrids has enabled an efficient energy management in the system with installation of renewable energy sources. As one of the representative models of microgrid, various studies on campus microgrids (CMGs) have been conducted. In operation of CMG, various energy consumption resources and renewable energy are considered to minimize overall cost or peak power in the system. However, most of conventional researches only deal with performance analysis in terms of simulation by collecting data from the different environments. In this case, there are lack of consideration in power regulation or electricity cost which make it difficult to apply the researched energy operation technology to the actual power system. To solve the problem, this paper build a test-bed in an actual CMG environment and collect dataset through the various IoT sensors. In addition, uncertainties that occur through the various power resources are analyzed and used to derive net energy consumption scenarios. In this way, we propose a new cost optimal energy management model with the detailed analysis of power generation and consumption using various auxiliary IoT devices. Based on the real-world datasets from the implemented CMG, we show that the proposed analytical models and energy management model are feasible for actual environments. With satisfying the constraints, we show that the daily electricity cost could be reduced up to 2.16% and peak power is reduced up to 3% compared to the case without considering the uncertainties in CMG.

Optimal economic-emission planning of multi-energy systems integrated electric vehicles with modified group search optimization

The present paper aims to present a comprehensive multi-objective optimization model for energy management in local multi-energy systems in the presence of plug-in electric vehicles (PEVs), seeking to achieve the maximized profit of the operators of the local multi-energy systems and minimized CO2 emission at the same time. This problem involves technology transfer in local multi-energy systems and finding the PEVs charge/discharge optimization strategies in order to maximize the operators' profit and, in the meantime, reduce the CO2 emission. It can be solved by formulating a multi-purpose objective and can be dealt with by formulating a multi-objective programming problem through accurate modeling of mutual dependencies between the energy carriers. To solve this problem, a Modified Group Search Optimization (MGSO) algorithm is used based on the decomposition system. By the proposed structure, the local and global search is improved significantly. As indicated by the results of the effectiveness test of the optimization framework for maximizing the operator's profit and, meanwhile, reducing the CO2 emission, this objective is achievable through optimal coordination of multiple energy carriers in local multi-energy systems and effective management of the flexibility collected at both supply and demand sides.The simulation has been investigated in different scenarios. Obtained numerical analysis shows that the base case signifies the best case in terms of maximization of the local multi-energy systems operator’s profit through the optimized charging and discharging approaches of the PEVs. Also, in all scenarios, the profit of operator reductions by 5%–15% as compared with the base case. On the other hand, the best case scenario from an environmental point of view is provided by a scenario categorized with electric vehicles excluding under environmental optimization. In fact, CO2 emissions are reduced 7% compared to the emissions of base case.

Optimal energy management of a retrofitted Rubber Tyred Gantry Crane with energy recovery capabilities

In this work, an optimal energy management model for the grid-powered electric RTG, with a battery storage system, is developed. The aim of the model is to reduce the operation cost, by minimizing the component linked to the maximum demand charges from the grid, as well as the component linked to the Time of Use (ToU) pricing structure. As a case study, a RTG crane operating in South Africa has been selected. The load profile, size the battery storage system, ToU tariff, as well as the maximum demand charges, are used as input for the developed model. Simulations, for a complete RTG handling cycle, have been conducted to evaluate the techno-economic performances of the developed model, used to optimally dispatch the power flow in the system during the different phases of operation. Three main configurations have been simulated as energy sources for the RTG crane, namely, the exclusive supply from the grid, grid/battery hybrid system without energy recovery during the lowering phase and grid/battery hybrid with energy recovery, during the lowering phase.As compared to the baseline, the simulation results have shown that using the proposed model shows a possible 50.36, 60.6 and 64.4% cost reduction, per full handing cycle, are possible in off-peak standard and peak pricing period, for the selected winter day. The results also show that the maximum demand charges may be reduced by 45.20%.Further economic analyses have been conducted; and the results indicate that the proposed system successfully reduce electrical energy costs, the peak demand and outperforms each of the presented baselines.

Optimal energy management of a hybrid diesel generator and battery supplying a RTG crane with energy recovery capability

In this paper, an optimal energy management model for a RTG crane supplied by a hybrid diesel generator/battery system is developed. The aim of the model is to reduce the energy cost spending and CO2 emission by minimizing the amount of fuel consumed by the diesel generator, and maximizing the potential energy recovered through the regenerative braking during the container lowering phase. As a case study, a 40 tonnes RTG crane operating in South Africa has been selected. The demand profile, size of the diesel generator as well as of the battery storage system are used as input to the model developed. Simulations, for a complete RTG handling cycle, have been conducted to evaluate the techno-economic performances of the developed model use to optimally dispatch the power flow in the system during the different phases of operation.As compared to the baseline case where the diesel generator is used alone to handle the same demand, the simulation results for the selected day of operation have shown that using the proposed model, a 40.6% reduction in the operation cost as well as CO2 emission is achievable in the case of the proposed system without energy recovery; while 82.17% is achievable in the case the energy recovery is included. Looking further into the stochastic nature of the demand, the analysis on a year of operation have revealed that 76.04% in operation cost can be potentially saved using the proposed system. The result of the true payback period analysis has shown that the overall investment cost may be recovered in 1.36 years. Additionally, it can be seen from the results that the peak power demand on the diesel generator has been reduced, this can assist in reducing the power rating and the initial cost of the diesel generator.