Distributed Energy Management System Research Articles

Advances in emerging digital technologies for energy efficiency and energy integration in smart cities

Advances and fast development in emerging digital technologies trigger the next generation revolution in energy areas and smart cities, while roles and mechanisms of digital technologies for smart and sustainable transition is unclear. Furthermore, energy flexibility enhancement in intermittent renewable-stochastic demand power management and survival capability in minimizing frequency of power outrage are still not clear, when suffering from climate change and extreme events with emerging digital technologies. In this study, advances in emerging digital technologies have been systematically and comprehensively reviewed, in terms of current development status and mechanisms for energy efficiency and energy integration in energy-efficient systems. Afterwards, roles of energy digitalization technologies are provided for high-efficiency, low-carbon and intelligent building energy systems, including artificial intelligence for dynamic performance predictions, advanced model predictive controls and optimisations of nonlinear systems (e.g., PVs, heat pumps, heat recovery systems and multi-energy storages). Furthermore, digital twin-based building energy digitalization technologies are applied for 3D modeling, monitoring, real-time visualization and virtual reality interaction. Frontier multi-agent based distributed energy systems are comprehensively proposed with multi-agent energy management, including RE-battery-building-EV, RE-building-hydrogen vehicles, and both centralised and distributed energy management systems. Considering the multi-energy system capability for power management (intermittent renewable energy and energy demands) and survival capability when suffering from high-impact and low-probability events, both energy flexibility and energy resilience with energy digitalization technologies are interconnected, for climate change adaption and internet of energy. This study provides a systematic and comprehensive review on emerging digital technologies for energy efficiency and energy integration in smart cities, providing guidelines on sustainable and smart transitions with multi-agent based distributed energy management and energy digitalization technologies.

Hierarchically Distributed Energy Management in Distribution Systems: An Error-Tolerant and Asynchronous Approach

Hierarchically Distributed Energy Management in Distribution Systems: An Error-Tolerant and Asynchronous Approach

CASL: A Novel Collusion Attack Against Distributed Energy Management Systems

Recently, the security issues of smart grids have received wide attention. In particular, false data injection attacks against distributed energy management systems (DEMSs) are considered of high importance as they are able to cause economic losses or even damages to system stability in subtle ways. Existing studies have identified many such attacks and proposed corresponding countermeasures. However, in this paper, we show that DEMS is still insecure and in risk of economic loss by proposing and analyzing a novel attack called Collusion Attack between a Storage and a Load (CASL). In CASL, a distributed energy storage device colludes with a load by injecting an abnormal extra power supply to the load in a secret way through using two secret functions. In this way, the colluding pair behaves as normal ones in both computational and communicational viewpoints. We theoretically prove the convergence of DEMS in presence of the attack, and derive an upper bound of the social welfare losses of DEMS caused by CASL. We demonstrate the effectiveness of CASL through simulations.

Coordinated Dispatch Optimization between the Main Grid and Virtual Power Plants Based on Multi-Parametric Quadratic Programming

Virtual power plants (VPPs) are a critical technology for distribution systems that can integrate various renewable energy resourcescontrollable loads and energy storage systems into one specific power plant through a distributed energy management system. This paper proposes a coordinated dispatch optimization model between the main grid and VPPs aiming to minimize both the power generation cost and total system active loss. When the time of the equivalent dispatching model is not divisible due to the existence of a time coupling constraint inside the VPPs, this model can obtain the global optimal solution through iteration between the main grid and the VPPs. By employing multi-parametric quadratic programming to obtain accurate critical domains and optimal cost functions, the convergence speed and stability are significantly improved. Additionally, a reactive power and voltage optimization technique leveraging the generalized Benders decomposition is presented for the coordination of the main grid and the VPPs. Moreover, the impact of distributed energy resource (DER) clusters on the main grid was studied, from which we proved that the proposed approach can expeditiously abate energy production expenditure and system active dissipation whilst enhancing the system equilibrium.

Smart residential distribution energy management system with integration of demand response and Aggregator

Smart residential distribution energy management system with integration of demand response and Aggregator

Distributed Finite-Time Cooperative Economic Dispatch Strategy for Smart Grid under DOS Attack

This paper proposes an energy management strategy that can resist DOS attacks for solving the Economic Dispatch Problem (EDP) of the smart grid. We use the concept of energy agent, which acts as a hub for the smart grid, and each EA is an integrated energy unit that converts, stores, and utilizes its local energy resources. This approach takes into account the coupling relationship between energy agents (EA) and utilizes the Lyapunov function technique to achieve finite-time solutions for optimization problems. We incorporate strategies to resist DOS attacks when analyzing finite-time convergence using the Lyapunov technique. Based on this, a finite convergence time related to DOS attack time is derived. The integral sliding mode control strategy is adopted and the Lyapunov method is used to analyze it, so that the algorithm can resist DOS attacks and resist external disturbances. Through theoretical analysis, it is shown that the strategy is capable of converging to the global optimal solution in finite time even if it is attacked by DOS. We conducted case studies of six-EA and ten-EA systems to verify the effectiveness of this strategy. The proposed strategy has potential for deployment in distributed energy management systems that require resilience against DOS attacks.

Development of a Platform for Distributed Energy Resources Management on the Basis of a Digital Twin

The paper discusses the development of a platform for distributed energy resources management based on digital twins. The platform use cases include demand response, electric vehicle charging, peer-to-peer energy trading, storage scheduling, virtual power plant, and so on. Thanks to the digital twin, the platform can perform the use cases controlling either real operation-stage equipment or virtual design-stage simulation models. The platform offers mass distributed energy resources owners and operators to improve the power supply quality (including stability), reduce costs (including transaction overhead), and gain emerging market opportunities (including participation in various aggregators' programs). Software and equipment vendors are interested in the platform's capability to quickly assemble distributed energy management systems almost without programming. The digital twin and the platform are designed with the viewpoint-based approach established by the international systems engineering standard ISO/IEC/IEEE 42010. The typical power system digital twin architecture is described. The major kinds of mathematical models as part of digital twins are presented: physical models based on numerical solutions of differential equations and optimization problems, machine learning models, knowledge-based models. The interoperability of such heterogeneous models is ensured on the basis of the ontological model of distributed energy. The platform architecture is represented from three key viewpoints: functional, information, and software. To formalize and ultimately automate the integration of heterogeneous models, we propose novel mathematical methods of model-based system engineering based on category theory, including universal constructions and the multicomma. The multicomma category is shown to be constructed using standard product, exponent, and pushout constructions, which makes it possible to establish a number of its practically significant properties.

Improved unscented Kalman filter based interval dynamic state estimation of active distribution network considering uncertainty of photovoltaic and load

State estimation of active distribution network (ADN) plays an important role in distribution energy management system. The increase penetration of distributed generations, especially the distributed photovoltaic (PV), in ADN leads to high uncertainty of ADN’s operation and the state of the ADN varies with the variation of the PV output power. For the uncertainty of PV power output, an interval dynamic state estimation (IDSE) method, which estimates the interval of ADN state variables is proposed in this paper. Firstly, considering the slow computation speed of the Unscented Kalman Filter (UKF), the square root UKF is used to predict the real-time operating level of the state variables. Secondly, since the power output of PV has features of the variation randomly, the neural network-based prediction intervals is employed to predict the power output interval of PV. Finally, the normal fluctuation range of ADN state is modelled as a bilevel non-linear programming problem to perform IDSE, which in turn monitors the operating state of ADN. The proposed method is evaluated on the IEEE 33-node and IEEE 123-node systems, respectively. The test results demonstrate that the dynamic status of the ADN can be tracked accurately using the proposed method.

Distributed operational management of microgrids: a second order dual update approach

This paper develops a Distributed Energy Management System (EMS) to optimally allocate electric and thermal power production in a polygenerative microgrid. The EMS problem is formulated as a multiperiod convex optimization problem and solved using AL-SODU, a new distributed algorithm based on the augmented Lagrangian method with second order dual updates. The proposed AL-SODU algorithm significantly outperforms state of the art algorithms employing first order dual updates, with 15-times speedup in convergence speed. A case study of the EMS based on AL-SODU is conducted on the Smart Polygeneration Microgrid (SPM) located on the Savona Campus of University of Genova (Italy). The EMS determines the optimal schedules for electric and thermal power plants every 15-minutes. This real time scheduling of the microgrid is enabled by the Al-SODU algorithm, which solves the scheduling problem in 1.86s.

Blockchain-Based Optimization of Distributed Energy Management Systems with Real-Time Demand Response

Blockchain-Based Optimization of Distributed Energy Management Systems with Real-Time Demand Response

A robust distributed energy management system for the coordinated operation of rural multi‐microgrids

A robust distributed energy management system for the coordinated operation of rural <scp>multi‐microgrids</scp>

Decentralised strategy for energy management of collaborative microgrids using multi‐agent system

In this study, the concept of collaborative microgrids (MGs) with shareable resources is introduced. The developed concept enables the householders of an isolated district or neighbourhood to cooperate and communicate intelligently in order to establish a reliable and proprietary MG. Therefore, a multi-agent system solution for a distributed energy management system is designed. The proposed approach should first supply households while maintaining MG stability. Second, it offers designers decent programing flexibility and ensures system adaptability when households are added or removed. Three agents are developed, the nanogrid (NG) agent which handles the centralised energy management of the NG and messages the Master agent with the necessary information. The Master agent is responsible for the decentralised management of energy over the microgrid and controls the switches. The management strategy using multi-agent systems aims to encourage customers considered as NG to contribute energy to the MG. The strategy relies on the implementation of a point-based remuneration model in which the collected points depend on the contribution. The results highlight the importance of the proposed approach, which can be exploited to connect and supply isolated facilities intelligently.

Multi-objective flexibility disaggregation to distributed energy management systems

Flexibility from stationary batteries and electric vehicles allow home energy management systems to optimally consume local photovoltaic generation. The aggregated flexibility potential of a set of energy management systems can further contribute to a common objective, e.g., peak shaving at the transformer station. This paper formalizes the (non-convex) goals of involved stakeholders when disaggregating a flexibility request to a pool of energy management systems as multi-objective problem. The proposed genetic algorithm optimizes for the resource aggregator ( minimizing cost and maximizing delivery probability ), the grid operator ( minimizing grid losses ), the resource provider ( maximizing autarky and self-consumption ), and a fairness provider ( maximizing fairness among participants ). A disaggregation schedule that satisfies the objectives of all stakeholders can be chosen a posteriori from the Pareto-optimal solution set, which is demonstrated with a case study on the IEEE 906 low voltage test feeder.

A Review on Blockchain Technology for Distribution of Energy

The alternative energy generation sources have increased drastically from centralized systems to distributed systems which increases the stability of energy distribution management systems and reduces the distribution cost as well. On the other hand, it reduces the probability of major area electricity blackout chances and decreases the energy distribution loss. For proper distribution and management of energy, there are different types of advanced technologies like artificial intelligence, and the Internet of Things (IoT) available, but a blockchain automated system is one of the best choices and is highly recommended. Various aspects of blockchain technology and energy management system have been discussed in this review paper where a total number of 423 journal papers, articles, and online information sources have been reviewed in the initial stage, and finally, 63 published research articles have been selected for review. There are several topics, including technology overview in energy management systems, blockchain application of energy trading, blockchain technology implementation challenges, distributed energy management system with Ethereum, and a conclusion with some recommendations have been discussed. Blockchain and Distributed Ledger Technology (DLT) are highly transparent, authenticate, and secure systems that can be used for distributing the energy between distributor and consumer without an intermediator which increases the overall efficiency of the system. This paper aims to highlight the blockchain and distributed ledger technology and how it works as well as optimize the transaction processing cost among the participants of the consortium network. This paper will make a significant contribution to the new research work and in the field of energy management systems.

Distribution Locational Marginal Price Based Transactive Energy Management in Distribution Systems with Smart Prosumers—A Multi-Agent Approach

This work proposes a distribution locational marginal price (DLMP)-based transactive energy (TE) framework for distribution systems with enthusiastic or smart prosumers. The framework uses a multi-agent system (MAS) as the basis on which the proposed TE model, i.e., distribution locational marginal price (DLMP) based TE management system (DTEMS), is implemented. DTEMS uses a novel metric known as the nodal earning component, which is determined by the optimal power flow (OPF) based smart auction mechanism, to schedule the TE transactions optimally among the stakeholders by alleviating the congestion in the distribution system. Based on the individual contributions to the congestion relief, DTEMS ranks the prosumers and loads as most valuable players (MVP) and assigns the energy trading price according to the category of the player. The effectiveness of the proposed TE model is verified by simulating the proposed DTEMS for a modified 33 bus radial distribution system fed with various plug-able energy resources, prosumers, and microgrids.

A Novel Deep Reinforcement Approach for IIoT Microgrid Energy Management Systems

Introducing Deep Learning in the Industrial Internet of Things (IIoT) brings many benefits, such as network resilience and bandwidth usage reduction. In this work, we propose an innovative reinforcement learning architecture to implement distributed energy management systems for microgrids. The architecture is based on novel reinforcement learning and on time series prediction. The designed reinforcement learning uses classical recurrent neural networks instead of the habitual SAR (State Action Reward) method that most of the recent bibliography considers. We applied various techniques (Exact resolution, Rule-Based, Q-Learning, and our designed reinforcement learning) on a distributed IIoT energy control architecture. The proposed method has shown better results compared to the exact resolution and the Q-Learning algorithm. It results in fast learning systems with a small number of training samples. We identified and tested several management strategies. Integer Linear Programming (ILP) optimal expressions and strategy-based implementations are derived. We utilize the obtained results to train the recurrent neural network. Comparative results are very encouraging and prone to a generalization of our approach instead of the classical methods.

A Linear Stochastic Formulation for Distribution Energy Management Systems Considering Lifetime Extension of Battery Storage Devices

Recently, the number of Battery Energy Systems (BESs) connected to the grid has grown significantly. These assets can alleviate some operational issues such as demand surges and occasional power fluctuations associated with the Renewable Energy Sources (RESs) connected to the grid. Nonetheless, both overcharging and frequent usage severely affect their health status and shorten their life expectancy. In this paper, an Energy Management System (EMS) framework with a linearised algorithm and in-depth analysis on BES life extension is presented, which optimises the techno-economic aspects of an Active Distribution Network (ADN) connected to RESs. By applying a mathematical linearisation formulation, a Mixed-Integer Linear Programming (MILP) model is proposed for linearising the Optimal Power Flow (OPF) problem. This technique, which has the merit of fair accuracy while having high speed, is used for scheduling BESs to increase their durability and decrease grid costs. To consider the inherent uncertainty associated with demand and RES generation, a two-stage Stochastic Programming (SP) method is implemented in the proposed model. In terms of battery Loss of Health (LoH) assessment, a linearised battery lifetime method is introduced. Ultimately, a modified 33-bus radial distribution test system with a day-ahead Real-Time Pricing (RTP) program was chosen to apply the proposed algorithm and assess its efficiency.

A Homomorphic Encryption-Based Private Collaborative Distributed Energy Management System

This article investigates the privacy issues in the distributed energy management system (EMS) of smart distribution systems and microgirds. A novel private collaborative distributed energy management system (P-CoDEMS) is proposed to solve the AC optimal power flow (ACOPF) problem in a distributed and private fashion. The proposed P-CoDEMS algorithm is based on an original primal dual subgradient distributed optimization technique and a state-of-the-art fully homomorphic encryption algorithm. The convergence and optimality of the proposed P-CoDEMS algorithm are evaluated on four representative systems. Simulation results indicate that the proposed P-CoDEMS algorithm can accurately solve the ACOPF problem in a fully distributed way while preserving individual agent privacy.

Integration of Prosumers with Battery Storage and Electric Vehicles Via Transactive Energy

This paper presents a novel transactive energy approach that uses the utility's distributed energy management system, the home energy management systems in the residential customers and the existing inter-communication infrastructure to: 1) enable the electric utilities to identify the negative impacts of integrating local distributed energy resources (such as rooftop solar photovoltaic) and electric vehicles; 2) provide voltage support, transformer management and phase balancing to mitigate such impacts (utilities’ objectives); and 3) create a day-ahead local energy market whose participants are the homeowners with home battery storage. In this market, the homeowners compete among each other through a dynamic Bertrand game to maximize their payoffs (customer's preferences). The results have demonstrated the capability of the proposed transactive energy approach to resolve the conflicting objectives between the utility and the customers by improving the service voltage, extending the lifetime of the transformers, and reducing the voltage unbalance, while increasing the profits for the homeowners through the energy arbitrage without sacrificing the homeowner's convenience.

A compact model to coordinate flexibility and efficiency for decomposed scheduling of integrated energy system

The conflict between inflexible operation of high-efficient cogeneration system and demand for flexibility in power grid has engendered severe renewable energy curtailment, which raises a requirement of multi-energy management. Nowadays, researchers have focused on flexibility improvement, but rarely considered energy conversion efficiency of cogeneration systems based on the 2nd law of thermodynamics. This paper establishes a compact model of cogeneration systems by taking nonlinear energy conversion and heat transfer constraints of combined heat and power units into account, and further proposes a two-stage heat and power dispatch procedure. The lower stage conducts plant-level operation optimization to obtain the compact model, i.e., quantitation of the best trade-off relations between energy supply flexibility and conversion efficiency as well as the power generation feasible region in each cogeneration system. The upper stage formulates a convex optimal power flow problem based on the compact model to achieve the global coordination between energy supply flexibility and conversion efficiency in integrated energy system. To apply the model, we introduce a multi-energy management system consisting of a central power-grid dispatch system and multiple distributed plant-level energy management systems. Optimization results reveal that the proposed method saves 11.65% heat consumption comparing with traditional method.