Hierarchical energy management system for multi-microgrid coordination with demand-side management

In the recent literature, the architecture of energy internet was proposed to replace the current smart grid in the future. However, the energy sharing concept and the uncertainties of intermittent energy sources will cause the short-term energy management for the future energy internet much more complicated than current centralized control energy management for traditional power generation system. To the best of our knowledge, no researcher has addressed on the above-mentioned short-term energy management problems for the future energy internet so far. Accordingly, a hierarchical day-ahead energy management system based on the architecture of energy internet is proposed in this work to tackle the complex energy management problems. We first collect electricity consumption data from smart appliances used in households and predict power generating capacity of renewable energy sources at the microgrid level. Then, regional energy routers are employed to schedule the usage of electricity for the customers by considering the efficiency of the use of distributed renewables. The experimental results show that the proposed system can mitigate the dependency on traditional power plants effectively, and balance peak and off-peak load of electricity market.

Electrical modelling of a grid-connected battery energy storage system via EMS and BMS data

Integration of demand side and supply side energy management resources for optimal scheduling of demand response loads – South Africa in focus

This paper presents a novel power flow optimization strategy for a Grid Connected microgrid (MG) equipped with a Battery Energy Storage System (BESS), namely a Li-Ion battery pack. A BESS can be employed to perform several functionalities, related to different user requirements, such as power stability, peak shaving, optimal energy trading, etc. In the proposed system the MG is composed by an aggregation of distributed power generators and loads and a BESS is adopted to manage the power over-production/over-demand in real time, in order to maximize the prosumer profit looking at the current energy prices and the BESS State of Charge (SOC). The Energy Management System (EMS) is based on a Fuzzy Logic Controller (FLC) with a suitable rule inference system designed by an Expert Operator (EO). The control strategy is tested with different power profiles and BESS capacities in order to verify its effectiveness and limits. Furthermore, the FLC has been optimized by a Genetic Algorithm to increase the total profit exploiting the BESS as energy buffer. The optimization results have been compared to the initial FLC designed by the EO, taking into account both the profit and the deterioration of the BESS measured through a suitable battery stress index.

Energy management systems (EMSs) form an important part of many Demand-Side Management (DSM) projects implemented on South African mines. Sustained EMS performance is only possible with proper system maintenance. However, it is challenging to maintain a large number of EMSs remotely. One of the main challenges associated with sustained EMS performance is changing production strategies. Although these changes require prompt revision of the EMS control strategy, energy services companies (ESCOs) typically use the daily EMS performance to assess the need for corrective maintenance. Thus, to improve the efficiency of EMS maintenance, a supervisory system was developed to facilitate reactive maintenance of different EMS technologies. This system prompts EMS maintenance based on diagnostics of key EMS components, the specific control strategy, and overall EMS performance. Results from two case studies using the system showed an increase in the EMS operational availability from 87% to 97%. The average EMS performance also increased from 1.7 MW to 2.6 MW. Seven additional implementations also improved average EMS operational availability from 72% to 95%, resulting in a combined performance of 19.2 MW compared to the 15.5 MW target. The abovementioned results prove that the developed supervisory system is able to facilitate sustained performance of a growing number of EMSs, by increasing the efficiency of the maintenance process.

This paper presents a market-oriented energy management system (EMS) for a hybrid power system composed of a wind energy conversion system and a battery energy storage system (BESS). The EMS is designed as a real-time model predictive control (MPC) system. The EMS dispatches the BESS to achieve the maximum net profit from the deregulated electricity market. Furthermore, the EMS aims at expanding the BESS lifetime by applying typical and practical constraints in the MPC problem on both the daily number of cycles (DNC) and depth of discharge (DOD). The MPC constraint optimizer is designed to tune the lifetime constraints optimally. It guarantees the optimal economic profit by finding the optimal DNC and DOD to achieve the maximum market revenue from energy arbitrage with the minimal expended-life cost. The effectiveness of this work is verified by comparison with a conventional MPC used in previous works. Simulation is conducted using real wind power and market data in Alberta, Canada.

The design and implementation of an energy-management system (EMS) applied to a residential direct current microgrid (DC-µG) is presented in this work. The proposed residential DC-µG is designed to provide a maximum power of one kilowatt by using two photovoltaic arrays (PAs) of 500 W, a battery bank (BB) of 120 V–115 Ah, a supercapacitor module of 0.230 F and a bidirectional DC–AC converter linked to the AC main grid (MG). The EMS works as a centralized manager and it defines the working operation mode for each section of the DC-µG. The operation modes are based on: (1) the DC-link bus voltage, (2) the generated or demanded power to each section of the DC-µG and (3) the BB’s state of charge. The proposed EMS—during the several working operation modes and at the same time—can obtain the maximum energy from the PAs, reduce the energy consumption from the main grid and keep the DC-link bus voltage inside a range of 190 V ± 5%. The EMS and local controllers are implemented by using LabVIEW and NI myRIO-1900 platforms. Moreover, experimental results during connection and disconnection of each DC-µG sections and different on-the-fly transitions are reported, these results focus on the behavior of the DC bus, which shows the DC bus robustness and stability. The robustness of the DC-µG is demonstrated by maintaining a balance of energy between the sources and loads connected to the DC bus under different scenarios.

In practice, a directional converter integrated into the battery energy storage systems - an inverter, which only supplies energy to the network for frequency and voltage regulation when necessary, and cannot receive energy from the network. Increasing the reliability of the stability of the electric power system will be especially effective when in the battery energy storage systems will be used a Bi-directional AC/DC Converter, which in turn includes a rectifier and an inverter, which will supply the grid through the energy management system and also consume energy from it if necessary through the upgraded SCADA/EMS. The high-level architecture of the Supervisory Control and Data Acquisition systems/Energy Management System includes a number of capabilities, including a dynamic stability analysis module, also "High numerical aperture extreme ultraviolet lithography - lithography of advanced semiconductor materials" - "High-NA-EUV lithography" provides opportunities for manufacturing such microcircuits, each nanocell includes a nanotransistor and a nanocapacitor, and their chipset size is 1 nanometer. The mentioned rate of scientific development provides an opportunity to create such technologies that will give wide opportunities to modern the battery energy storage systems to fully respond to the requirements that arise during the continuous operational management of the country's electric power system.

Photovoltaic (PV) is a highly feasible solution for modern renewable energy-powered residential buildings in terms of deployment and cost reduction of utility bills. The installation of solar PV systems along with optimal battery energy storage systems (BESS) size is the most popular energy cost minimization solution and will continue to increase rapidly in the coming years considering the European Union (EU) framework for nearly zero energy buildings (nZEBs). The current methods lack BESS size optimization and a comprehensive solution to charge/discharge BESS from PV and the grid. The main goal is to be self-sufficient and sustainable while having minimal dependence on the electrical grid. Therefore, this paper presents an efficient energy management model and optimal size of the BESS as two key factors to effectively minimize the total energy consumption cost of the nZEBs while having a minimum dependence on the grid. The energy management system is developed using linear programming and solved using simplex and interior-point methods. In addition, a heuristic algorithm is presented to determine the optimized charging and discharging schedule for nZEBs. A detailed techno-economic analysis of the proposed system is conducted for the whole year (covering all four seasons summer, winter, spring, and autumn) considering three common residential building cases and three different electricity pricing methods. We determined that seasonal electricity pricing is the favorable and economical option to schedule charging and discharging of BESS from the grid in several terms such as, minimum total hours of grid usage, the maximum number of charging hours of BESS from the solar PV system, maximum BESS discharging hours to sell energy, the minimum number of BESS charging hours from the grid, maximum number of discharging hours for energy usage within nZEBs, maximum revenue earned, and peak electrical load reduction for the grid.

Battery energy storage system (BESS) is the key element to integrate a distributed generation (DG) unit into a microgrid. This paper presents a microgrid consisting of singlephase photovoltaic (PV) arrays which function as the primary DG units and a BESS to supplement the intermittent PV power generation and demand variations in the microgrid. An energy management system is proposed to coordinate the operations of the microgrid during grid-connected and islanded modes of operation. The BESS is incorporated into the microgrid to deal with power imbalance and peak load demand during grid-connected operation and to compensate for any power shortage during islanded operation. The controller design for the voltage source inverter employs a model predictive control (MPC) algorithm which enables faster dynamic response. MATLAB/Simulink is used to simulate the proposed microgrid under different test scenarios. The simulation results show that the operations of the BESS in the microgrid can be coordinated effectively using the proposed control system to ensure stable operation of the overall microgrid.

<span>This paper proposes a new optimal operation of Microgrids (MGs) in a distribution system with wind energy generators (WEGs), solar photovoltaic (PV) energy systems, battery energy storage (BES) systems, electric vehicles (EVs) and demand response (DR). To reduce the fluctuations of wind, solar PV powers and load demands, the BES systems and DR are utilized in the proposed hybrid system. The detailed modeling of WEGs, solar PV units, load demands, BES systems and EVs has been presented in this paper. The objective considered here is the minimization of total operating cost of microgrid, and it is formulated by considering the cost of power exchange between the main power grid and microgrid, cost of wind and solar PV energy systems, cost of BES systems, EVs and the cost due to the DR in the system. Simulations are performed on a test microgrid, and they are implemented using GAMS software. Various case studies are performed with and without considering the proposed hybrid system.</span>

The Introduction of Smart Meters (SMs) is one of the fundamental changes for the intelligent power grid. SMs provide input data from the electricity customers, which might also be a local electricity producer, also called prosumers. This article proposes novel strategies using an energy management system (EMS) to enhance economic value for the prosumers and for the network operators in terms of reliability performance and flexibility of the electricity power grid. These strategies, referred to as behind the meter strategies, could be influenced, e.g., using a battery energy storage system (BESS), plug-in electric vehicles (PEVs), and various alternatives of local electricity generation like solar photovoltaic (PV) or wind power. Using remote sensing and the reading functionality of SMs, the BESS can be intelligently managed by an EMS that uses the BESS resource for multiple ancillary services, thereby generating additional value from the present resource.

In this paper, a nonlinear decentralized double‐integral sliding mode controller (DI‐SMC) is designed along with an energy management system (EMS) for the DC microgrid (DCMG). This DCMG includes having a hybrid energy storage system (HESS) that incorporates a battery energy storage system (BESS) and supercapacitor energy storage system (SCESS) while the load demand is met through the power generated from solar photovoltaic (SPV) units. First, dynamical models of each subsystem of DCMGs such as the SPV system, BESS, and SCESS are developed to capture highly nonlinear behaviors of DCMGs under various operating conditions. The proposed nonlinear DI‐SMC is then designed for each power unit in DCMGs to ensure the desired voltage level at the common DC‐bus and appropriate power dispatch of different components to fulfill the load requirement of the DCMG. On the other hand, an energy management system (EMS) is designed to determine the set point for the controller with an aim of ensuring the power balance within DCMGs under various operating conditions where the overall stability is assessed using the Lyapunov theory. Simulation studies along with the processor‐in‐loop validation, including a comparative study with a proportional‐integral (PI) controller, verify the applicability and effectiveness of the EMS‐based DI‐SMC under different operating conditions of the DCMG.

Intermittency of renewable generation entails a large amount of flexibility and spinning reserve to enable secure and stable operation of the power system. The additional imposed cost due to accommodating renewables jeopardizes the economic value of renewable energy projects. Dispatchable renewable generation can help reducing the need for flexibility and spinning reserve. The optimal scheduling unit of an energy management system (EMS) for a dispatchable hybrid renewable power plant is described. It uses forecasting and optimization techniques to optimize the power output from a wind farm, a solar PV system and a battery energy storage system (BESS) such that dispatchable renewable generation is realized. Hence, the hybrid renewable power plant can participate in the interval-ahead electricity market in a similar fashion to conventional units and get dispatched. In addition to reducing the requirement for flexibility and reserve, a dispatchable hybrid renewable power plant can participate in the reserve market using part of the stored energy and any uncommitted power. This is a major leap towards an even higher penetration of renewables in power systems since it directly targets one of the main disadvantages of renewables. A case study is carried out to demonstrate the potential for integrating Sultanate of Oman’s Harweel wind farm with a solar PV system and a BESS to provide dispatchable hybrid renewable generation, with the aid of the introduced optimal scheduling unit of the EMS.

Dynamic load demand affects output power to load, which may fail to meet ICE standards 61892–1 for offshore oil and gas installation. High power consumption in offshore oil rig requires both large gas and wind turbine generation that are unable to react quickly to enhance transient stability. Hence, an energy management system (EMS) is designed with a battery energy storage system (BESS) to replace partial output power from the gas turbine onboard the oil rig for optimal transient response. Our designed EMS differs from the current state of art by not using a low-pass filter so as to improve rapid response from BESS, while enhancing output power quality to load. Our EMS is validated with maximum transient voltage and frequency deviations in simulation to illustrate improved transient stability results.