Local Energy Management System Research Articles

Distributed management of energy communities using stochastic profile steering

This paper presents a novel approach for distributed management of energy communities. The proposed method utilizes a stochastic profile steering algorithm as a greedy heuristic. The optimization process considers random parameters such as local forecasted demand, photovoltaic (PV) production, and the initial energy of electric vehicles (EVs) as embedded scenarios. Profile steering coordinates the flexible electricity assets within an energy community by determining the contribution of each prosumer’s profile to the average value of the objective function. It iteratively selects the prosumer that contributes the most until no further improvements can be made. This process scales linearly with the number of controllable prosumers and can achieve various community-level objectives, such as maximizing self-sufficiency or minimizing aggregated cost-of-energy, even when dealing with non-convex optimization problems for modeling each prosumer’s local energy management system. The outcome of the proposed method optimizes the average value of the community’s objective while ensuring that grid limitations are met within a specified probability. The proposed method is evaluated through simulations involving small-scale communities (5 households) and large-scale communities (100 households). The results demonstrate the efficiency, flexibility, and scalability of the proposed method, as well as its ability to reschedule the aggregated demand to ensure that grid limits are not violated with at least a 95% probability.

Design of an intelligent hybrid diagnosis scheme for cyber-physical PV systems at the microgrid level

With focusing on solar photovoltaic (PV) systems operating at microgrid (MG) level, this study investigates the impacts of different physical faults and cyberattacks as well as designing an intelligent faults/attacks detection and diagnosis system. The ability to quickly identify and diagnose faults/attacks enables local controllers and energy management system to accommodate or mitigate the negative effects of physical faults or cyberattacks in a MG. This allows the MG to carry on operating without experiencing any significant interruptions. In this regard, the current paper investigates a hybrid AC/DC MG made up of different distributed energy resources (DER), including solar PV arrays, wind turbines, and battery energy storage systems. An intelligent hybrid diagnosis (IHD) scheme is introduced for online monitoring and diagnosing the data to reflect the real-time status of the PV system operating at the MG level. This hybrid system uses three parallel units, including a “fuzzy inference system (i.e., a rule-based unit)”, a “power spectrum estimator (i.e., a signal-based unit)”, and an “adaptive neuro-fuzzy inference system (i.e., a model-based unit)”. A realistic MG benchmark model with a wide range of operating conditions and dynamic electrical loads in the presence of potential MG disturbances is used to demonstrate the high performance and resilience of the proposed IHD scheme under various types of faults/attacks scenarios.

Short-term photovoltaic power production forecasting based on novel hybrid data-driven models

The uncertainty associated with photovoltaic (PV) systems is one of the core obstacles that hinder their seamless integration into power systems. The fluctuation, which is influenced by the weather conditions, poses significant challenges to local energy management systems. Hence, the accuracy of PV power forecasting is very important, particularly in regions with high PV penetrations. This study addresses this issue by presenting a framework of novel forecasting methodologies based on hybrid data-driven models. The proposed forecasting models hybridize Support Vector Regression (SVR) and Artificial Neural Network (ANN) with different Metaheuristic Optimization Algorithms, namely Social Spider Optimization, Particle Swarm Optimization, Cuckoo Search Optimization, and Neural Network Algorithm. These optimization algorithms are utilized to improve the predictive efficacy of SVR and ANN, where the optimal selection of their hyperparameters and architectures plays a significant role in yielding precise forecasting outcomes. In addition, the proposed methodology aims to reduce the burden of random or manual estimation of such paraments and improve the robustness of the models that are subject to under and overfitting without proper tuning. The results of this study exhibit the superiority of the proposed models. The proposed SVR models show improvements compared to the default SVR models, with Root Mean Square Error between 12.001 and 50.079%. Therefore, the outcomes of this research work can uphold and support the ongoing efforts in developing accurate data-driven models for PV forecasting.

Privacy preserving renewable energy trading system for residential communities

This paper proposes a new trading framework for enabling energy trading between end energy customers and a small-capacity Renewable Energy Plant (REP) in urban environments. To relieve the communication burden incurred by a large number of end customers who submit bids to purchase energy from the REP, the energy trading process is designed on a community basis. The end customers are grouped into multiple communities. With this approach, the proposed energy trading system coordinates the REP, the community energy management system in each community, and the local energy management system of each customer. The homomorphic encryption technology is integrated into the energy trading framework to enable the energy trading to be performed and settled without exposing individual customer’s bidding energy purchase price and amount values. Extensive numerical simulations are conducted to validate the effectiveness of the proposed system.

Design and optimal energy management of community microgrids with flexible renewable energy sources

Energy communities is a new, but already successful prosumer model of the local energy systems' construction. It is based on distributed energy sources and the electricity consumers’ flexibility who are the members of the community. In search of the most effective ways to interact within themselves and with the external energy system, local energy communities become platforms for exciting experiments in the field of new energy practices including local markets for flexibility, building cooperative microgrids, achieving energy autonomy, and many others. This work aims to present a unified approach to building and optimally managing the community microgrids with an internal market, given the social, environmental, and economic benefits of a particular location of such a community. A new modeling framework is introduced, based on bilevel programming and reinforcement learning, for structuring and solving the internal local market of a community microgrids, composed of entities that may exchange energy and services among themselves. The overall framework is formulated in the form of a bilevel model, where the lower level problem clears the market, while the upper level problem plays the role of the community microgrid operator (Community EMS). We strengthen the traditional bilevel problem statement by the local energy management system (Local EMS) introduction based on Monte-Carlo tree search algorithm. Our approach makes it possible to enable interaction of the local control systems for microgrids with the community microgrid operator as part bilevel programming problem solution. Numerical results obtained on the real test case of the microgrid community for the settlements located in the Transbaikal National Park (Russia), which include various renewable energy sources (wind, solar power, biomass gasifiers) and storage devices, show reduction of the LCOE index from 20% to 40% and improving the quality of electricity supply to the analyzed settlements.

Operation of coupled multi-owner district heating networks via distributed optimization

The growth of district heating and cooling (DHC) networks introduces the possibility of connecting them with neighbouring networks. Coupling networks can save costs by reducing operating hours of peak load or backup boilers, or free up production capacity for network expansion. Optimization-based energy management systems (EMS) already provide operators of individual DHC networks with solutions to the unit commitment and economic dispatch problem. They are especially useful for complex networks with multiple producers and integrated renewable energy sources, where incorporating forecasts is important. Time-dependent constraints and network capacity limitations can easily be considered. For coupled networks, a centralized optimization would provide a minimum with respect to an objective function which can incorporate fuel costs, operational costs and costs for emissions. However, the individual coupled networks are generally owned by different organizations with competing objectives. The centralized solution might not be accepted, as each company aims to optimize its own objective. Additionally, all data has to be shared with a centralized EMS, and it represents a single point of failure. A decentralized EMS may therefore be a better choice in a multi-owner setting. In this article, a novel decentralized EMS is presented that can handle multi-owner structures with cooperative and non-cooperative coupling. Each local EMS solves its own optimization problem, and an iterative Jacobi-style algorithm ensures consensus among the networks. The distributed EMS is compared to a centralized EMS based on a representative real-world example consisting of three coupled district heating networks operated by two companies.

Multi‐objective auto‐encoder deep learning‐based stack switching scheme for improved battery life using error prediction of wind‐battery storage microgrid

For any wind power generation system, battery energy storage is a suitable backup power unit for ensuring greater functionality by compensating the prediction error due to the variable nature of generation. For designing local energy management system, the hierarchical operation of distributed generation controllers, BES life, etc. is directly influenced by the prediction error profile. To design an effective local energy management system, it is imperative to obtain wind power generator control reference influencing the overall system stability and reducing the deterioration of life and power loss of the integrated battery energy storage system due to increased temperature. Thus, an efficient local energy management system for the doubly fed induction generator battery energy storage system is proposed here for the robust minimization of the prediction error and to increase controller responses and the life of the battery. A novel deep learning robust multilayer multi-kernel extreme learning machine autoencoder algorithm is proposed here in order to obtain an improvement in the prediction error profile. This prediction algorithm produces optimal model generalization along with the minimization of reconstruction errors and uses simple matrix inversion for prediction in comparison with more complex deep learning neural networks. A new secondary controller is proposed to address the degradation of the life of the battery energy storage system due to increased temperature/power loss profile under prediction error. A model reference-based battery temperature model, associated with multi-objective optimization-based temperature tolerance dynamic stack reconfiguration of battery stacks is incorporated in the proposed secondary controller. This secondary controller-based local energy management system operation is targeted towards satisfying local load demand and grid dispatch, while ensuring an optimized battery stack performance under wind power generation discrepancies. In addition, a new adaptive slope-based primary controller is designed for effective power sharing between the battery energy storage system and wind power generator based on % state of the charge of the energy storage system. Also, an independent distributed generator controller is studied in an elaborate manner for the DC link stability of the DC-DC converter and voltage source converter for grid synchronization control.

Privacy-Preserving Energy Management of a Shared Energy Storage System for Smart Buildings: A Federated Deep Reinforcement Learning Approach.

This paper proposes a privacy-preserving energy management of a shared energy storage system (SESS) for multiple smart buildings using federated reinforcement learning (FRL). To preserve the privacy of energy scheduling of buildings connected to the SESS, we present a distributed deep reinforcement learning (DRL) framework using the FRL method, which consists of a global server (GS) and local building energy management systems (LBEMSs). In the framework, the LBEMS DRL agents share only a randomly selected part of their trained neural network for energy consumption models with the GS without consumer’s energy consumption data. Using the shared models, the GS executes two processes: (i) construction and broadcast of a global model of energy consumption to the LBEMS agents for retraining their local models and (ii) training of the SESS DRL agent’s energy charging and discharging from and to the utility and buildings. Simulation studies are conducted using one SESS and three smart buildings with solar photovoltaic systems. The results demonstrate that the proposed approach can schedule the charging and discharging of the SESS and an optimal energy consumption of heating, ventilation, and air conditioning systems in smart buildings under heterogeneous building environments while preserving the privacy of buildings’ energy consumption.

Fair-Optimal Bilevel Transactive Energy Management for Community of Microgrids

The inappropriate mechanism designs for demand response (DR) in the community of microgirds (CoMGs) may cause massive problems, such as increase of consumers’ costs, rebound peaks, and thereby lack of optimality in the network. In this article, a bilevel energy management system (EMS) is proposed to tackle the challenges associated with DR programs for CoMGs. The current structure successfully models users’ behavior and dissatisfaction in the first level of optimization to develop best DR program for each of them. Moreover, in the second level, power system constraints are taken into account to prevent voltage and current deviation from their statutory limits. Each user is assumed to be part of a microgrid (MG) whose operation is controlled and optimized through its local EMS in the first level. On the other hand, the overall operation of all MGs is delegated to the whole system operator, which acts as the central EMS (CEMS) in the second level. An iterative transactive energy management method is proposed by CEMS to fairly limit the excess power of the MGs one day ahead for voltage and current regulation. The obtained results indicate the effectiveness of the proposed structure in preventing discomfort issues, voltage deviation and creation of the rebound peaks in the system.

Real-time Energy Management for PV–battery–wind based microgrid using on-line sequential Kernel Based Robust Random Vector Functional Link Network

This paper comprises of an effective Local Energy Management System (LEMS) implementing the Generalized Power Prediction Model (GPPM) for the uncertain performance of various Distributed Generations of a microgrid. The primary DGs considered to compensate intermittency is Wind Power Generation System (WPGS) and Photovoltaic (PV) along with Battery Energy Storage (BES). Solar and wind power are considered here as prediction targets and to cope with the intermittent nature of the Renewable Energy Sources (RESs), Power calculation from the RES data is included in the proposed prediction model. In order to establish arobust reduction in the predicted error, areal-time Online Sequential Kernel based Robust Random Vector Functional Link Network (OS-KRRVFLN) prediction algorithm is developed. Here, the initial randomness of the proposed OS-KRRVFLN is reduced (robust performance) by Hampel’s cost function (eP based) minimization. The PV-BES and WPGS based DGs are designed to be operated under certain uncertainties like PV arc, bus faults etc. as uninterrupted power supply with minimum grid power dependency. An efficient Distributed Adaptive Droop (DAD) control is thus opted as Primary Controller (PC). Addictiveness of DAD’s operational region is estimated while compensating eP”-. The coordination of GPPM with DAD-PC based accurate reference estimation for Independent DG Controllers (IDGCs) operation is considered as LEMS operation.

Federated Reinforcement Learning for Energy Management of Multiple Smart Homes With Distributed Energy Resources

This article proposesa novel federated reinforcement learning (FRL) approach for the energy management of multiple smart homes with home appliances, a solar photovoltaic system, and an energy storage system. The novelty of the proposed FRL approach lies in the development of a distributed deep reinforcement learning (DRL) model that consists of local home energy management systems (LHEMSs) and a global server (GS). Using energy consumption data, DRL agents for LHEMSs construct and upload their local models to the GS. Then, the GS aggregates the local models to update a global model for LHEMSs and broadcasts it to the DRL agents. Finally, the DRL agents replace the previous local models with the global model and iteratively reconstruct their local models. Simulation results obtained under heterogeneous home environments indicate the advantage of the proposed approach in terms of convergence speed, appliance energy consumption, and number of agents.

Optimal hierarchical energy management scheme for networked microgrids considering uncertainties, demand response, and adjustable power

In this study, a novel strategy for transactive energy in the networked microgrids (MGs) is presented. This strategy consists of three stages: local optimisation, global optimisation, and re-local optimisation. In the local optimisation, the daily cost of each MG is minimised by local energy management systems (EMSs). The results of the local optimisation are sent to the central energy management system (CEMS), and global optimisation is performed by CEMS. Finally, the primary scheduling of MGs is modified in re-local optimisation. The adjustable power concept is introduced to provide power exchange among MGs and external units. Various uncertainties, such as loads, renewable generations, and market prices are deliberated to the model. To do so, the worst condition of forecasted values of renewable generations and load consumptions are considered to assign a safety margin. Besides, the uncertainty of market prices is implemented by using the variance matrix based on forecasted and real values of market prices in D days in advance. To evaluate the efficiency of the proposed model, it is tested on a standard case study. The simulation results show that the cost of MGs in the grid-connected mode is improved effectively.

Stochastic Predictive Energy Management of Multi-Microgrid Systems

Next-generation power systems will require innovative control strategies to exploit existing and potential capabilities of developing renewable-based microgrids. Cooperation of interconnected microgrids has been introduced recently as a promising solution to improve the operational and economic performance of distribution networks. In this paper, a hierarchical control structure is proposed for the integrated operation management of a multi-microgrid system. A central energy management entity at the highest control level is responsible for designing a reference trajectory for exchanging power between the multi-microgrid system and the main grid. At the second level, the local energy management system of individual microgrids adopts a two-stage stochastic model predictive control strategy to manage the local operation by following the scheduled power trajectories. An optimal solution strategy is then applied to the local controllers as operating set-points to be implemented in the system. To distribute the penalty costs resulted from any real-time power deviation systematically and fairly, a novel methodology based on the line flow sensitivity factors is proposed. Simulation and experimental analyses are carried out to evaluate the effectiveness of the proposed approach. According to the simulation results, by adopting the proposed operation management strategy, a reduction of about 47% in the average unplanned daily power exchange of the multi-microgrid system with the main grid can be achieved.

Cooperative energy management system for networked microgrids

Abstract This paper proposes a hybrid Energy Management Systems (EMS) architecture based on canonical coalition games for cooperative power exchange management of networked microgrids, interconnected with the main grid through a macro station. A central EMS and local EMSs perform three main processes: scheduling process, monitoring and rescheduling process, and benefits distribution process. A value function verifying the superadditivity property, which guarantees the grand coalition is the best coalition structure, and a computationally efficient non-linear scheduling model, which allows obtaining the power exchange strategy that minimizes transmission and transformation power losses, are defined. The scheduling process uses the scheduling model to generate a cooperative schedule for a rolling horizon. In order to enhance the performance of networked microgrids, the monitoring process uses efficiency thresholds for monitoring the schedule execution to identify schedule disruptions. For preserving the private information of microgrids, each local EMS only informs the central EMS of their state profiles, which specifies its power surplus or deficit for each time period of the scheduling horizon. The benefit-distribution process uses a Core-based model for distributing saved power loss obtained as a result of the power exchanged during the schedule execution. By comparing the proposed approach with a coalition formation game-based algorithm recently reported in the literature, it is concluded that the problem of power exchange management of networked microgrids should be modeled as a canonical coalition game and not as a coalition formation game. Results obtained from a prototype testing of the proposed EMS are presented.

Intelligent energy management in microgrid using prediction errors from uncertain renewable power generation

This study proposes an efficient local energy management system (LEMS) based on the generalised power prediction model for the uncertain operation of renewable distributed generations (DGs)-based microgrid. Photovoltaic with battery energy storage, and wind power generation are considered as primary DGs to compensate intermittency. Conventional direct power prediction models are limited to specific DG applications, where the plant data acquisition system is a necessity. Solar irradiance and wind speed are considered here as prediction targets to cope with such additional expenditure for a microgrid. To ensure a robust reduction in prediction error (ep), a short-term prediction model is developed by virtue of the proposed robust regularised random vector functional link network. A maximum-likelihood estimator using Huber's cost function is employed to attain the robustness of this model. Further, a direct renewable energy source-power calculation is opted to address model accuracy under local uncertainties. The LEMS operation is completed by compensating ep with distributed adaptive droop-based primary controllers for multi-DG based microgrid. To ensure the performance of the prediction model, solar irradiance, wind speed and power at different atmospheric conditions (seasonal volatility) and time span (i.e. 5, 10 and 60 min) have been implemented in MATLAB and real time.

A hierarchical energy management strategy for interconnected microgrids considering uncertainty

Coordinating the operation of neighboring microgrids is a promising solution for the problem of growing penetration of renewable-based microgrids into the power system. In this paper, a hierarchical stochastic energy management system is proposed for operation management of interconnected microgrids. At the upper-level, a central entity is responsible for coordinating the operation of microgrids. Based on the energy scheduling made at this level, the power reference values to be exchanged within the microgrids network and between the microgrids and the main grid are calculated and communicated with the local energy management systems. At the lower-level, a decision making approach based on chance-constrained model predictive control is adopted for local operation management of each microgrid taking into account different sources of uncertainties. The results show that the proposed strategy provides the microgrids with the opportunity of exploiting maximum available capacity in the network. Consequently, the microgrids dependency on the main grid will be reduced and some important performance indices such as multi-microgrid system cost and real-time power deviations will be improved.

Energy Internet Access Equipment Integrating Cyber-Physical Systems: Concepts, Key Technologies, System Development, and Application Prospects

This paper systematically proposes a novel concept of energy Internet access equipment (AE) integrating cyber-physical systems (CPSs). First, based on the concept and characteristics of energy Internet AE, we comprehensively investigate its global system development, including overall framework and functions design, core hardware equipment development, and software system design and development. Among these, the hardware part containing five major types of equipment is used to achieve a unified connection of various distributed energy equipment; the software part, consisting of three categories of system platforms (i.e., local energy management system, cloud Web page system, and user-side APP), is applied to integrate energy management and distributed coordination control. Moreover, we carry out an initial practice for this proposed energy Internet AE. Based on this, finally, we prospect future application scenarios for the energy Internet AE from multiple aspects. As a unified interface platform, the proposed energy Internet AE is directly applied for end-users to connect various energy supply and utilization equipment to the energy Internet, so as to monitor and coordinate the distributed energy equipment in real time and, finally, to realize the bidirectional circulation of energy flow and information flow in smart grid. The biggest innovation of this paper lies in comprehensively proposing a novel concept of energy Internet AE from the perspective of the CPS, with an aim of hoping to provide some thoughts for end-users to access the energy Internet efficiently and conveniently through such equipment in the future.

Scalable Local Energy Management Systems

Commercial buildings have been identified as a major contributor of total global energy consumption. Mechanisms for collecting data about energy consumption patterns within buildings, and their subsequent analysis to support demand estimation (and reduction) remain important research challenges, which have already attracted considerable work. We propose a cloud based energy management system that enables such analysis to scale to both increasing data volumes and number of buildings. We consider both energy consumption and storage to support: (i) flattening the peak demand of commercial building(s); (ii) enable a “cost reduction” mode where the demand of a commercial building is reduced for those hours when a “triad peak” is expected; and (iii) enables a building manager to participate in grid balancing services market by means of demand response. The energy management system is deployed on a cloud infrastructure that adapts the number of computational resources needed to estimate potential demand, and to adaptively run multiple what-if scenarios to choose the most optimum configuration to reduce building energy demand.

Load Shedding Analysis in Local Energy Management Systems

Load Shedding Analysis in Local Energy Management Systems

Distributed Optimization Framework for Energy Management of Multiple Smart Homes With Distributed Energy Resources

This paper proposes a distributed optimization algorithm for scheduling the energy consumption of multiple smart homes with distributed energy resources. In the proposed approach, the centralized optimization problem for home energy management is decomposed into a two-level optimization problem, corresponding to the local home energy management system (LHEMS) at the first level and the global home energy management system (GHEMS) at the second level. The controllable household appliances (e.g., air conditioner and washing machine) are scheduled in the LHEMS within the consumer’s preferred appliance scheduling and comfort level, while the energy storage system and power trading between households are scheduled in the GHEMS. In the simulation study, the proposed distributed algorithm shows almost equivalent performance to the centralized algorithm in terms of the electricity cost and the consumer’s comfort level. The impact of different network topologies on the proposed algorithm is also analyzed, and the result provides insight into the selection of the optimal network configuration in view of the consumer’s electricity cost saving.