Energy Management Strategy of Microgrids Based on Benders Decomposition Method

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This paper discusses an optimal energy management system for microgrids, taking into account distribution power flow and dynamic loads, in presence of storage units and all associated constraints, aiming to reduce microgrid costs under two grid-connected and islanded modes. Getting the unit commitment, the microgrid energy management problem is introduced as a mixed integer nonlinear problem (MINLP). Since solving MINLP problems is complex and time consuming, a linearization technique is applied for simplification of the problem as a mixed integer linear programming (MILP) problem. Then, the Benders decomposition method is used to reach an efficient and accurate answer. The model proposed is implemented on a 14-bus microgrid including conventional and renewable distributed resources, storage units, and dynamic loads. The results indicated fair and fast performance of the proposed model

Forecast Based Consensus Control for DC Microgrids Using Distributed Long Short-Term Memory Deep Learning Models

In a microgrid, renewable energy sources (RES) exhibit stochastic behavior, which affects the microgrid continuous operation. Normally, energy storage systems (ESSs) are installed on the main branches of the microgrids to compensate for the load-supply mismatch. However, their state of charge (SoC) level needs to be balanced to guarantee the continuous operation of the microgrid in case of RES unavailability. This paper proposes a distributed forecast-based consensus control strategy for DC microgrids that balances the SoC levels of ESSs. By using the load-supply forecast of each branch, the microgrid operational continuity is increased while the voltage is stabilized. These objectives are achieved by prioritized (dis)charging of ESSs based on the RES availability and load forecast. Each branch controller integrates a load forecasting unit based on long short-term memory (LSTM) deep neural network that adaptively adjusts the (dis)charging rate of the ESSs to increase the microgrid endurability in the event of temporary generation insufficiencies. Furthermore, due to the large training data requirements of the LSTM models, distributed extended Kalman filter algorithm is used to improve the learning convergence time. The performance of the proposed strategy is evaluated on an experimental 380V DC microgrid hardware-in-the-loop test-bench and the results confirm the achievement of the controller objectives

Arbitrage strategy of renewable-based microgrids via peer-to-peer energy-trading

In this paper, an arbitrage strategy is proposed for renewable-based microgrids (MGs) to overcome the volatile behavior of renewable energy sources (RESs) such as photovoltaic and wind in a newly emerged business space in which peer-to-peer (P2P) energy-trading in transactive energy markets (TEMs) set up between a day-ahead market (DAM) and real-time markets (RTMs). To identify arbitrage opportunities created from the price difference between the P2P and real-time trades, a bi-level risk-constrained stochastic programming with interval coefficients (BRSPIC) is presented. In the first stage of the decision-making, scenarios are employed to deal with the DAM prices uncertainties. In the second stage, P2P energy-trading competition is modelled by a bi-level programming based on non-cooperative leader-follower games. While the social welfare of peers is maximized at the lower level, the MG maximizes its profit at the upper level. By getting closer to real-time, interval coefficients are considered in the third stage to cope with the uncertainties of RESs and loads, as well as RTM prices. The conditional value-at-risk (CVaR) is enforced the model to control the risk of profit variability. By using Karush-Kuhn-Tucker (KKT), the BRSPIC is transformed into a single level optimization. Then, it is linearized and solved by a mixed-integer linear programming (MILP) solver. By evaluating the proposed model on a test system, it is evident that the MG increases more than 3.1% of its profit by the arbitrage strategy. By considering CVaR, a fully risk-averse decision decreases the profit of MG by 27%, although it would be so conservative decision

Assessment of energy storage systems as a reserve provider in stochastic network constrained unit commitment

Recently, the provision of the reserve from energy storage systems (ESSs) is introduced as a source for ancillary services to address the uncertainties of renewable power generations. The performance of ESSs is analysed while they are applied as a provider of regulation reserves. It has been revealed that previous stochastic models neglect the impact of corrective dispatches, related to the provision of regulation reserves, on the energy level stored in the ESSs, which can lead to large deviations. This study coordinates the stored energy of ESSs to be feasible regarding the dispatches in the base schedule and rescheduling within scenarios. Also, the wind speed fluctuations are considered as the source of uncertainty, and scenarios of wind energy are generated using the Weibull distribution function. The IEEE 24-Bus standard test system is applied for the examination of the proposed model. The results show that the proposed model can manage the performance of ESSs in rescheduling within scenarios, while the coordinated reserve provision of ESSs can remove the concerns about insufficient stored energy of ESSs

Tree-partitioning as an emergency measure to contain cascading line failures

This paper proposes to replace controlled islanding, which is a defense mechanism against cascading failures, by tree partitioning whereby some of the tie-lines connecting the clusters are still connected in such a way that the cluster-level graph forms a tree. Tree-partitioning prevents line failures from spreading between clusters, similarly as for islanding, but keeps the clusters connected. That results in three main advantages. Power transfers between the clusters can still take place, helping to balance each cluster and limiting any necessary load shedding. Fewer lines are cut, which reduces the shock to the system. There is no need to re-synchronize the clusters after the emergency. This paper offers a simple graph-theoretic justification for tree-partitioning, rather than one based on the spectral analysis of network Laplacian proposed in the literature. It also proposes a two-stage methodology, which utilizes spectral clustering, for splitting a network into tree-connected clusters. Test results performed on the 118 node IEEE test network have confirmed the usefulness of the methodology

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 review on demand-side tools in electricity market

With the advent of restructuring in the electricity markets, the Supply-side quickly adapted to the new environment, whereas, the story in the demand side has been different. Demand side dealt with the electric energy as a commodity available to the necessary extent. This caused the Supply-side to realize that the demand side would admit to purchase electric energy at any price, and this resulted in the advent of bidding strategies in the Supply-Side, known as “hockey-stick bidding”. The most important result was transfer of the demand side assets to the Supply-side. After a while, the demand side noticed the self-sloppy condition, therefore looked for tools to deal with these threats. This subject is examined in this paper

Review of emerging advanced smart charging flexibility business models

Electric vehicle sales across all transport modes have had a steady growth over the last decade, and mass electric mobility will soon become a reality. In Europe, this represents an opportunity to introduce higher shares of variable renewable energy into the generation mix. However, a shift to mass electromobility needs to be accompanied by extensive integration of advanced smart electric vehicle charging, which could serve growing mobility needs while supporting the power system through a series of possible flexibility services. Such services need yet to mature, and its synergistic business models to be better understood in terms of value streams they will deliver and to whom. This paper investigates a group of such business models, particularly linking EV and/or homeowners, building managers, and network operators

A decentralized blockchain-based energy market for citizen energy communities

Despite the fact that power grids have been planned and utilized using centralized networks for many years, there are now significant changes occurring as a result of the growing number of distributed energy resources, the development of energy storage systems and devices, and the increased use of electric vehicles. In light of this development, it is pertinent to ask what an efficient approach would be to the operation and management of future distribution grids consisting of millions of distributed and even mobile energy elements. Parallel to this evolution in power grids, there has been rapid growth in decentralized management technology due to the development of relevant technologies such as blockchain networks. Blockchain is an advanced technology that enables us to answer the question raised above. This paper introduces a decentralized blockchain network based on the Hyperledger Fabric framework. The proposed framework enables the formation of local energy markets of future citizen energy communities (CECs) through peer-to-peer transactions. In addition, it is designed to ensure adequate load supply and observe the network’s constraints while running an optimal operation point by consensus among all of the players in a CEC. An open-source tool in Python is used to verify the performance of the proposed framework and compare the results. Through its distributed and layered management structure, the proposed blockchain-based framework proves its superior flexibility and proper functioning. Moreover, the results show that the proposed model increases system performance, reduces costs, and reaches an operating point based on consensus among the microgrid elements