Decentralized Energy Management Research Articles

Blockchain-based decentralized energy management platform for residential distributed energy resources in a virtual power plant

The advent of distributed energy resources (DERs), such as distributed renewables, energy storage, electric vehicles, and controllable loads, brings a significantly disruptive and transformational impact on the centralized power system. It is widely accepted that a paradigm shift to a decentralized power system with bidirectional power flow is necessary to the integration of DERs. The virtual power plant (VPP) emerges as a promising paradigm for managing DERs to participate in the power system. In this paper, we develop a blockchain-based VPP energy management platform to facilitate a rich set of transactive energy activities among residential users with renewables, energy storage, and flexible loads in a VPP. Specifically, users can interact with each other to trade energy for mutual benefits and provide network services, such as feed-in energy, reserve, and demand response, through the VPP. To respect the users’ independence and preserve their privacy, we design a decentralized optimization algorithm to optimize the users’ energy scheduling, energy trading, and network services. Then we develop a prototype blockchain network for VPP energy management and implement the proposed algorithm on the blockchain network. By experiments using real-world data trace, we validated the feasibility and effectiveness of our algorithm and the blockchain system. The simulation results demonstrate that our blockchain-based VPP energy management platform reduces the users’ cost by up to 38.6% and reduces the overall system cost by 11.2%.

A Trusted and Privacy-Preserving Internet of Mobile Energy

The rapid growth in distributed energy sources on power grids leads to increasingly decentralised energy management systems for the prediction of power supply and demand and the dynamic setting of an energy price signal. Within this emerging smart grid paradigm, electric vehicles can serve as consumers, transporters, and providers of energy through two-way charging stations, which highlights a critical feedback loop between the movement patterns of these vehicles and the state of the energy grid. This paper proposes a vision for an Internet of Mobile Energy (IoME), where energy and information flow seamlessly across the power and transport sectors to enhance the grid stability and end user welfare. We identify the key challenges of trust, scalability, and privacy, particularly location and energy linking privacy for EV owners, for realising the IoME vision. We propose an information architecture for IoME that uses scalable blockchain to provide energy data integrity and authenticity, and introduces one-time keys for public EV transactions and a verifiable anonymous trip extraction method for EV users to share their trip data while protecting their location privacy. We present an example scenario that details the seamless and closed loop information flow across the energy and transport sectors, along with a blockchain design and transaction vocabulary for trusted decentralised transactions. We finally discuss the open challenges presented by IoME that can unlock significant benefits to grid stability, innovation, and end user welfare.

Decentralized Coordination of a Community of Electricity Prosumers via Distributed MILP

In this paper, the decentralized energy management of a community of prosumers is studied. The coordination of the prosumers participating in the community is cast as a mixed-integer linear program (MILP) with local and global coupling constraints. A dual decomposition method with a tightening of the coupling constraints is applied to solve the problem in a decentralized way. The resulting tightened problem provides feasibility guarantees for the obtained solutions. A receding horizon approach that exploits the structure of the problem is proposed to divide the problem in subproblems and solve them sequentially. The proposed approach ensures that the tightened problem is feasible under realistic conditions, whereas the existing methods may result in infeasible problems. Moreover, it may reduce the necessary constraint tightening compared to existing methods. Two setups are evaluated for the organizational structure of the community, one with a community coordinator and one without. Moreover, three different structures of the communication graph are analyzed for the setup without a coordinator. The simulation results demonstrate that the proposed method results in feasible problems that can be solved due to the reduced constraint tightening when other methods are not feasible. In the case of feasible problems, the optimality gap is comparable to that achieved with existing methods and even smaller for communities with a small number of participating prosumers.

Decentralized energy management for unbalanced networked microgrids with uncertainty

The energy management schemes in microgrids enhance the utilization of renewable energy resources and improve the reliability and resilience measures in the distribution networks. While microgrids operate autonomously, the coordination among microgrid and distribution network operators (DNOs) contributes to the improvement in the economics and reliability of serving the demand. Here, a decentralized energy management framework for the networked microgrids is proposed in which the interactions between the microgrid and DNOs are captured using the Benders decomposition technique. The proposed framework limits the information shared among these autonomous operators and facilitates decentralized energy management in the distribution networks. Furthermore, the unbalanced operation of the distribution network and microgrids, as well as the uncertainty in the operating modes of the microgrids, renewable energy resources, and demand, were addressed. Modified IEEE-34 bus and IEEE-123 bus distribution networks are used to validate the effectiveness of the proposed algorithm.

Decentralized Energy Management System for LV Microgrid Using Stochastic Dynamic Programming With Game Theory Approach Under Stochastic Environment

In this article, we propose a decentralized energy management system to minimize the daily operating cost of microgrid and the net charging cost of individual plug-in electric vehicles (PEVs) while maintaining comfort level and privacy. A novel approach based on stochastic dynamic programming (SDP) with N-person noncooperative game-theoretic approach is used for real-time scheduling of heterogeneous PEVs both ways-in the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) mode. We use the SDP method for full enumeration of all possible decisions while game theory provides quick convergence to Nash-equilibrium. The proposed approach considers uncertainties of renewable generation as well as load demand and offers autonomy to the microgrid operators and PEVs owners to optimize their objectives individually. The effectiveness of the proposed approach has been tested on modified LV CIGRE and modified IEEE 33 bus test networks. The results have been compared with the deterministic and stochastic bi-level optimization. The simulation results exhibit that the proposed algorithm outperforms the other two algorithms in terms of operating cost, incentive to the PEVs owner, robustness to uncertainties, and convergence. The operating cost reduction achieved for the modified CIGRE model and modified IEEE 33 bus with the proposed approach is 20.02% and 36.53%, respectively, as compared to the deterministic framework, whereas 7.64% and 16.35% are the values obtained in comparison with the bi-level approach.

A hierarchical and decentralized energy management system for peer-to-peer energy trading

This paper presents a novel Hierarchical and Decentralized Energy Management System which facilitates the Peer to Peer energy trading between prosumers in the community by coordinating the operation of distributed Home Battery Storage Systems and shiftable home appliances in a decentralized way to achieve a further reduction in the household energy costs for each house, compared to being operated individually (i.e. not being a part of the community). The hierarchical system consists of three levels: Selection level, Peer to Peer Management level, and Home Management level. First, the daily energy cost of each household is optimized individually using the lower layer. Then, the results are further improved through a peer to peer energy sharing algorithm in which house pairs are selected if they can achieve better reductions in operating cost through a joint optimization process. Finally, the optimal settings for the selected couples are obtained. An effective sensitivity analysis for the proposed management system is also introduced to study the effect of the size and the efficiency of the Home Battery Storage, the size of the PV generation, and the average annual household consumption on the economic performance of the householders in the community. The results obtained are based on real historic data for several prosumers in a real community system. The results show that the proposed energy management system guarantees a further reduction in the annual household energy costs for each house (up to 8.96%) when being operated as a part of a community, compared to being operated individually.

Microgrid Energy Management With Asynchronous Decentralized Particle Swarm Optimization

Controlling distributed energy resources (DERs) in low voltage microgrids is a challenging task for operators. The simultaneous operation of independent small-scale DER owners could compromise the operator’s hierarchical and centralized control to reach system stability and cost optimization. Recent Decentralized Energy Management (DEM) approaches provide flexibility for DERs control, but several existing solutions depend on powerful and expensive computer clusters and their ability to deal with a high burden of data in the communication channel. This work is motivated towards a DEM framework that involves independent DER owners while microgrid operator still maintains a hierarchical control philosophy. The framework must include a method to reduce the need of powerful computer clusters and depend on low bandwidth communications channel. Here, a multi-layered framework for every DER, consisting of physical, control, and agent layers for DEM is approached, where the agent layer participates in the energy management task. An Asynchronous Decentralized PSO (ADPSO) algorithm is proposed for the agent layer based on its primal characteristic: it can reach a consensus state between networked computing units by exchanging asynchronously only the state variable through the communications channel. The proposed solution allows the integration of DEM capabilities within the physical controller of the DERs, distinguishing it from other decentralized solutions. Easiness of implementation and low computational requirements are shown by performing DEM tests on single board computers. The tests show improved convergence rate, improved swarm diversity behavior and fast consensus reaching of DEM optimization.

Blockchain technology based decentralized energy management in multi-microgrids including electric vehicles

This paper proposes an energy scheduling mechanism among multiple microgrids (MGs) and also within the individual MGs. In this paper, electric vehicle (EV) energy scheduling is also considered and is integrated in the operation of the microgrid (MG). With the advancements in the battery technologies of EVs, the significance of Vehicle-to-Grid (V2G) is increasing tremendously. So, designing the strategies for energy management of electric vehicles (EVs) is of paramount importance. The battery degradation cost of an EV is also taken into account. Vickrey second price auction is used for truthful bidding. To enhance the security and trust, blockchain technology can be incorporated. The market is shifted to decentralized state by using blockchain. To encourage the MGs to generate more, contribution index is allotted to each prosumer of a MG and to the MGs as a whole, depending on which priority is given during auction. The system was simulated using IEEE 118 bus feeder which consists of 5 MGs, which in turn contain EVs and prosumers.

Systematic Categorization of Optimization Strategies for Virtual Power Plants

Due to the rapid growth in power consumption of domestic and industrial appliances, distributed energy generation units face difficulties in supplying power efficiently. The integration of distributed energy resources (DERs) and energy storage systems (ESSs) provides a solution to these problems using appropriate management schemes to achieve optimal operation. Furthermore, to lessen the uncertainties of distributed energy management systems, a decentralized energy management system named virtual power plant (VPP) plays a significant role. This paper presents a comprehensive review of 65 existing different VPP optimization models, techniques, and algorithms based on their system configuration, parameters, and control schemes. Moreover, the paper categorizes the discussed optimization techniques into seven different types, namely conventional technique, offering model, intelligent technique, price-based unit commitment (PBUC) model, optimal bidding, stochastic technique, and linear programming, to underline the commercial and technical efficacy of VPP at day-ahead scheduling at the electricity market. The uncertainties of market prices, load demand, and power distribution in the VPP system are mentioned and analyzed to maximize the system profits with minimum cost. The outcome of the systematic categorization is believed to be a base for future endeavors in the field of VPP development.

Supporting Decentralised Energy Management through Smart Monitoring Systems in Public Authorities

Energy infrastructure in large, multi-site organisations such as municipal authorities, is often heterogeneous in terms of factors such as age and complexity of the technology deployed. Responsibility for day-to-day operation and maintenance of this infrastructure is typically dispersed across large numbers of individuals and impacts on even larger numbers of building users. Yet, the diverse population of stakeholders with an interest in the operation and development of this dynamic infrastructure typically have little or no visibility of energy and water usage. This paper explores the integration of utility metering data into urban management processes via the deployment of an accessible “smart meter” monitoring system. The system is deployed in three public authorities and the impact of the system is investigated based on the triangulation of evidence from semi-structured interviews and case studies. The research is framed from three perspectives: the bottom-up micro-level (individual and local), the top-down macro-level (organisation-wide and strategic) and intermediate meso-level (community-focused and operation). Evidence shows that improved communication across these levels enables a decentralisation and joining-up of energy management. Evidence points to the importance of reducing the cognitive load associated with monitoring systems. Better access to information supports more local autonomy, easier communication and cooperation between stakeholders and fosters the conditions necessary for adaptive practices to emerge.

Decentralized multi-agent based energy management of microgrid using reinforcement learning

This paper proposes a multi-agent based decentralized energy management approach in a grid-connected microgrid (MG). The MG comprises of wind and photovoltaic resources, diesel generator, electrical energy storage, and combined heat and power generations to serve electrical and thermal loads at the lower-level of energy management system (EMS). All distributed energy resources (DERs) and customers are modelled as self-interested agents who adopt reinforcement learning to optimize their behaviours and operation costs. Based on this algorithm, agents have the capability to interact with each other in a distributed manner and find the best strategy in competitive environment. At the upper-level of EMS, there is an energy management agent that gathers the information of agents of lower-level and clears the MG electrical and thermal energy market in line with predetermined goals. Utilizing energy availability from different DERs and variety of customers’ consumption patterns, considering uncertainty of renewable generation and load consumption and taking into account technical constraint of DERs are the strengths of the presented framework. Performance of the proposed algorithm is investigated under different conditions of agents learning and using ε-greedy, soft-max and upper confidence bound methods. The simulation results verify efficacy of the proposed approach.

State-of-the-Art Integration of Decentralized Energy Management Systems into the German Smart Meter Gateway Infrastructure

The German Smart Meter Gateway (SMGW) infrastructure enables digital access to metering data and distributed energy resources by external parties. There are, however, various restrictions in order to guarantee the privacy of consumers, and strong security requirements. Furthermore, in the current state of development, there are still several challenges to overcome in order to implement demand side management (DSM) measures. In this paper, we present a prototype enabling DSM measures within the SMGW infrastructure, using the smart grid traffic light concept. The prototype implements an automated decentralized energy management system (EMS) that optimally controls an electric vehicle charging station. In the development of this prototype, we did not only evaluate five of the seven available SMGW devices, but also push the limits of the infrastructure itself. The experiments demonstrated the successful implementation of the intended DSM measure by the EMS. Even though there are technical guidelines standardizing the functionality of SMGWs, our evaluation shows that there are substantial differences between the individual SMGW devices.

A Decentralized Energy Management Strategy for a Fuel Cell–Battery Hybrid Electric Vehicle Based on Composite Control

Traditionally, the energy management objectives of a fuel cell-battery hybrid electric vehicle (FCBHEV) powertrain system is achieved by using the centralized control strategy, which suffers from poor flexibility, scalability, and reliability. What is worse, the system stability can hardly be guaranteed because of the serious interactions between the fuel cell (FC) as well as the battery converters and motor drive loads. In this article, a decentralized energy management strategy (EMS) based on a composite control scheme that combines mixed droop control and disturbance-observer-based control is proposed to achieve the dynamic load power allocation, extend the service life, improve the energy efficiency, enhance the robustness (i.e., realize the disturbance and uncertainty rejection), and guarantee the global stability of the FCBHEV powertrain system in a decentralized way simultaneously. First, the architecture of an FCBHEV powertrain system is described in detail. Then, analyses on the design and operating principle of the proposed EMS are deeply studied. Finally, a hardware-in-the-loop (HIL) real-time simulation system of a 50 kW FCBHEV powertrain system is established using the Typhoon HIL 602 device. The HIL simulation results verified the correctness of the theoretical analysis and effectiveness of the proposed EMS.

Mode-triggered droop method for the decentralized energy management of an islanded hybrid PV/hydrogen/battery DC microgrid

In a hydrogen-based DC microgrid (MG), the integration of hydrogen subsystem increases the system complexity and flexibility. Effective power split, bus voltage stability and reliable operation become significant control issues of hydrogen-based DC MG. This paper proposes a simple and effective decentralized energy management strategy based on a mode-triggered droop scheme for an islanded PV/hydrogen/battery DC MG. In a decentralized manner, the proposed decentralized energy management strategy is implemented through two control steps: mode divisions and droop control. For practical implementation purposes of the proposed energy management strategy, the mode divisions scheme autonomously divides this DC MG into eight operating modes based on the system local information; the adaptive droop control method controls the distributed generation units based on their droop relationships under different operating modes. In addition, an islanded DC MG hardware-in-the-loop (HIL) RT-LAB simulation platform and a lab-scaled experimental platform are built to validate the effectiveness of the proposed decentralized strategy. The simulation and experiment results show that the proposed energy management strategy has an efficient power distribution ability without communication link under various operating modes.

Electricity demand forecasting for decentralised energy management

Abstract The world is experiencing a fourth industrial revolution. Rapid development of technologies is advancing smart infrastructure opportunities. Experts observe decarbonisation, digitalisation and decentralisation as the main drivers for change. In electrical power systems a downturn of centralised conventional fossil fuel fired power plants and increased proportion of distributed power generation adds to the already troublesome outlook for operators of low-inertia energy systems. In the absence of reliable real-time demand forecasting measures, effective decentralised demand-side energy planning is often problematic. In this work we formulate a simple yet highly effective lumped model for forecasting the rate at which electricity is consumed. The methodology presented focuses on the potential adoption by a regional electricity network operator with inadequate real-time energy data who requires knowledge of the wider aggregated future rate of energy consumption. Thus, contributing to a reduction in the demand of state-owned generation power plants. The forecasting session is constructed initially through analysis of a chronological sequence of discrete observations. Historical demand data shows behaviour that allows the use of dimensionality reduction techniques. Combined with piecewise interpolation an electricity demand forecasting methodology is formulated. Solutions of short-term forecasting problems provide credible predictions for energy demand. Calculations for medium-term forecasts that extend beyond 6-months are also very promising. The forecasting method provides a way to advance a novel decentralised informatics, optimisation and control framework for small island power systems or distributed grid-edge systems as part of an evolving demand response service.

Robust Energy Management of Residential Nanogrids via Decentralized Mean Field Control

Energy management systems (EMS) are viable techniques to reduce the customers billing cost as well as enhancing the grid reliability and efficiency. Uncertainty and information privacy of users are serious concerns in designing EMS of a residential nanogrid with renewable energy resources. This paper addresses a decentralized energy management algorithm for day-ahead scheduling of residential nanogrids using the concept of Mean Field (MF). The aim of each user is to find its optimal demand strategy by minimizing an objective function consisting of energy consumption cost, battery degradation cost, and cost of user discomfort while meeting a suite of constraints. The strategy of a user affects the objective functions of other users through the electricity price; such interaction among the users is hence modeled as a game problem. Each user, without any information exchange with other users, sends its electricity demand to the utility company. The utility company broadcasts the aggregated demand as a common information of the grid (MF term) to all users. The robustness of the algorithm in presence of uncertainties in decision making of the clients and non-residential load prediction error is analytically verified. In particular, it is shown that the algorithm converges to $\epsilon$ -Nash equilibrium point of the game and $\epsilon$ uniformly converges to its minimum value as the population size of the users goes to infinity. Simulation results show the advantages of the proposed method.

A Decentralized Energy Management Strategy of Fuel Cell-Ultracapacitor Hybrid Vehicle

In this paper, a decentralized energy management strategy for the fuel cell-ultracapacitor hybrid vehicle. Without communication between the fuel cell unit and ultracapacitor unit, this strategy not only achieves fluctuating load power splitting and recycles braking energy, but also ensures the global stability of system, which is the novelty of this paper. Simulation results based on Matlab/Simulink show the effectiveness and feasibility of the proposed control strategy.

On the Decentralized Energy Management Strategy for the All-Electric APU of Future More Electric Aircraft Composed of Multiple Fuel Cells and Supercapacitors

In this article, a decentralized energy management strategy (EMS), which is derived from the conventional droop control, is proposed for the auxiliary power unit composed of multiple fuel cells (FCs) and supercapacitors (SCs) of a more electric aircraft. By utilizing a virtual inductor droop scheme to the FC converter and a virtual resistor droop scheme to the SC converter, the proposed EMS is able to split the load power automatically into low and high frequency components and allocate them to the FC and SC units, respectively. In addition, by optimally designing the droop parameters, accurate power sharing among different source units is achieved. The proposed strategy is not only suitable for extending the service life of the system by adding State of Charge (SoC) protection mechanism in the energy management, but also can improve the system reliability as it is compatible for the disconnection of some source units when fault occurs. The operational principle of the proposed EMS and the detailed system design methodology are elaborated, followed by which hardware-in-the-loop simulation study based on Typhoon HIL 602 is done to verify its validity.

A Decentralized Energy Management Strategy for a Fuel Cell/Supercapacitor-Based Auxiliary Power Unit of a More Electric Aircraft

In this paper, a decentralized energy management strategy (EMS), which is based on a modified droop control, is proposed for a fuel cell (FC)/supercapacitor (SC)-based auxiliary power unit (APU) of a more electric aircraft. The proposed EMS is able to split the load power automatically into low- and high-frequency components and allocate them to the FC and SC units, respectively. Since the proposed EMS is fully decentralized, it features high flexibility and scalability. Furthermore, the proposed strategy is not only suitable for extending the service life of the system because the protection and recovery of the state-of-charge of the SC have been considered in doing the energy management, but also can make the system accommodate the regenerative power in a lossless way. The operational principle of the proposed strategy, system design details and performance analysis are elaborated, followed by which experiments based on a 3 kW FC/SC-APU are conducted to verify its validity.

Stochastic robust optimization for smart grid considering various arbitrage opportunities

Because of power electronic advancements, nowadays Plug-in Electric Vehicles (PEVs) are capable to charge/discharge(absorb/inject) active(reactive) power. The storage capacity and the capability of bidirectional flowing active and reactive powers, lead PEVs to be contemplated as a viable option for energy arbitrage. By high penetration of PEVs, decentralized energy management of them, especially at peak hours causes serious problems to the network operation and service quality. Therefore, it is important PEVs to be controlled as integrated with microgrid (MG). Besides, PEVs uncertain behavior along with the prevalent uncertainties inherent to renewable resources and various pricing mechanism in electricity industry leaves the MG operators (MGOs) a challenging decision making. So, it is vital to be applied an efficient strategy in order to deal with this challenges. This paper proposes a centralized framework to co-optimize robust/stochastic optimization of MG with the PEVs energy arbitrage in both active and reactive powers exchange. The problem is a mix-integer non-linear programming (MINLP) problem, which is solved by GAMS software. The results of suggested model are investigated on IEEE 18-bus and IEEE 33-bus test systems.