Robust Voltage Unbalance Control With Inherent Current Sharing in Grid-Connected Microgrids

High reliability, Self-healing and performance optimization are key characteristics of microgrid systems. Photovoltaic systems (PVs) in the microgrid (MG) must be interfaced by smart electronic devices that provide the possibility to implement high control solutions. In addition, the PVs should be able to operate at the maximum power point (MPP) in order to extract the maximum power during atmospheric conditions changes. In term of participating to the grid regulation, the MG provides these ancillary services like a frequency stabilization. This paper presents a modeling and control of two Photovoltaic systems in AC Microgird connected to the power grid using double stage configuration, the active power and grid synchronization also grid current and DC voltage control are presented and discussed. The control structure is validated through dynamic simulations.

In recent years, a large number of wind power has been applied in the micro-grid (MG). Influenced by randomness characteristics of wind speed, the uncertainty in the power output of wind turbines imposes some safety and stability problems on the optimal energy management in MG. To address this problem, an expert energy management system (EEMS) considering wind power probability is developed in this study for optimal dispatching of a typical grid-connected MG. The EEMS composes of wind power probabilistic forecasting module, multi-objective optimization module and energy storage system (ESS) module. In the wind power forecasting module, wind power probabilistic forecasting based on complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and Gaussian process regression (GPR) is proposed in this study. To improve the forecasting results, CEEMDAN, an effective signal processing method, is employed to decompose the wind power data, then, the decomposed subseries are utilized as the inputs of GPR for probabilistic forecasting. A two-step solution methodology combining an efficient and effective improved multi-objective bat algorithm (IMOBA) with fuzzy set theory (FST) is put forward to solve the optimal dispatching problems. In the first step, IMOBA is developed to optimize the energy dispatching of EEMS by minimizing both economic cost and pollutant emissions simultaneously, and obtain a well-distributed set of Pareto optimal front (POF), then, FST is employed to identify the best compromise solutions from POF. Six operational scenarios of a typical grid-connected MG based one-POF-one-day and one-POF-one-hour dispatching schemes are constructed to investigate the effectiveness of the proposed strategy and provide more flexibility for decision makers. The results illustrate that EEMS can effectively schedule power generation and energy storage by considering economic cost and pollutant emission objectives simultaneously.

In recent years, in the face of China's increasingly serious environmental pollution and energy structure problems, the country has proposed the double carbon target of "peak carbon" by 2030 and "carbon neutral" by 2060, and microgrids, as a kind of small-scale power generation and distribution system with economy and environmental protection, can better absorb distributed energy. In the context of the dual carbon target, it is important to study the development mode and control strategy of microgrids energy. This paper firstly elaborates the advantages of combining microgrid and distributed energy, divides microgrid into grid-connected microgrid and stand-alone microgrid according to the operation mode, and gives the mathematical model of distributed energy. Based on the above, this paper studies grid-connected and stand-alone microgrids. Additionally, single-objective and multi-objective models under different strategies have been presented. The single-objective model aims to minimize operating costs, while the multi-objective model aims to minimize operating costs and carbon emissions simultaneously. Genetic algorithms have been employed to solve this problem, and MATLAB simulations have been used to analyze the models. The results of the simulations demonstrate that this strategy can significantly reduce carbon emissions with only a marginal increase in costs, thus validating the correctness and effectiveness of this approach.

Renewable electrical energy (such as: solar and wind energies) generation in microgrids (MGs), is gaining attention to reduce greenhouse gas emissions. Microgrid operators (MOs) aim to create self-sufficient, environmentally sustainable grids, increasing the capacity of renewable energy sources (RESs) by up to 100%. Despite of the benefits of this trend, challenges arise from non-controlled characteristics of these power generations and their seasonal variations, causing fluctuations and renewable energy curtailment. Although the technical solutions; such as: the demand response (DR) programs, and the conventional electrical energy storage systems (EESSs) can help, however those may face limitations in countries with high seasonal energy generation and consumption variations. This paper introduces cryptocurrency mining loads (CMLs) as innovative virtual energy storage systems (VESSs), named cryptocurrency energy storage systems (CESSs). It proposes a structure to store excess renewable energy in cryptocurrency units (CCUs) like Bitcoin (BTC). CESSs can be charged during off-peak intervals and, conversely, they discharge during high-demand periods to reduce the overall operational cost of MGs. Furthermore, it presents a new energy management system (EMS) formulation for the optimal operation of MGs in the presence of CESSs, providing an opportunity to generate additional electricity from RESs and to mitigate renewable energy curtailment. This paper explores the optimal operation conditions of both islanded and grid-connected MG with the proposed CESS. Utilizing a dataset from an island in Finland as a practical MG, its effectiveness is demonstrated through several case studies. The results of one case study in this paper demonstrate that the proposed CESS can decrease the operating cost of the MG by about 46.5%. Additionally, it is showed that by application of CESS the renewable energy curtailment is significantly reduced, and approached zero.

With the advent of smart grid theory, distribution networks can include different microgrids (MGs). Therefore, to achieve the desired technical and economic objectives in these networks, there is a need for bilateral coordination between their operators. In the following, by defining an energy management problem for them, it is predicted that the mentioned goals can be achieved. Therefore, this paper presents the hybrid flexible-securable operation (HFSO) of a smart distribution network (SDN) with grid-connected multi-microgrids using a two-layer coordinated energy management strategy. In the first layer, the microgrid (MG) operator is coordinated with sources, storages, and demand response operators. This layer models the HFSO method in the grid-connected MGs, which is based on minimizing the difference between the sum of operating cost of nonrenewable distributed generations and cost of energy received from the SDN, and the sum of flexibility and security benefits. It is constrained to AC optimal power flow, flexibility and voltage security constraints, operation model of sources and storages, and demand response. The second layer concerns coordination between the MG operators and the SDN operator. Its formulation is the same as that of the first layer, except that the HFSO model is used in the SDN according to MGs power daily data obtained from the first layer problem. The strategy converts the mixed-integer nonlinear programming to linear one to obtain the optimal solution with low calculation time and error. Moreover, stochastic programming models the uncertainties of load, energy price, and renewable power. Eventually, numerical results confirm the capability of the scheme to improve technical and economic indices simultaneously. As a result, by expecting the optimal operation for sources, storage, and responsive loads, it succeeded to enhance energy loss, voltage profile, and voltage security of the mentioned networks by up to 30%, 22%, and 5%, respectively, compared to power flow studies. In addition, there was enhancement in economic and flexibility status of the SDN and MGs.

An improved charging/discharging strategy of lithium batteries considering depreciation cost in day-ahead microgrid scheduling

microgrids (MGs) are a promising solution to global energy challenges. Robust and reliable operation while maximizing infeed from renewable sources and reducing the use of thermal generation are important objectives in the control of MGs. In this paper, two model predictive control strategies are applied to operate a grid-connected MG in the presence of bounded uncertainties in an optimal way. One is a certainty-equivalence and the other a minimax approach. They differ in the disturbance model used for renewable energy source infeed and load. To find an optimal operation strategy, a mixed-integer quadratic program is solved online. In a numerical case study, the results of two approaches are analyzed. The simulation shows, that compared to the performance of the certainty-equivalent case, the minimax approach is robust to disturbances and allows a secure operation of MGs in grid-connected mode.

Recently, significant development has occurred in the field of microgrid and renewable energy systems (RESs). Integrating microgrids and renewable energy sources facilitates a sustainable energy future. This paper proposes a control algorithm and an optimal energy management system (EMS) for a grid-connected microgrid to minimize its operating cost. The microgrid includes photovoltaic (PV), wind turbine (WT), and energy storage systems (ESS). The interior search algorithm (ISA) optimization technique determines the optimal hour-by-hour scheduling for the microgrid system, while it meets the required load demand based on 24-h ahead forecast data. The control system consists of three stages: EMS, supervisory control and local control. EMS is responsible for providing the control system with the optimum day-ahead scheduling power flow between the microgrid (MG) sources, batteries, loads and the main grid based on an economic analysis. The supervisory control stage is responsible for compensating the mismatch between the scheduled power and the real microgrid power. In addition, this paper presents the local control design to regulate the local power, current and DC voltage of the microgrid. For verification, the proposed model was applied on a real case study in Oshawa (Ontario, Canada) with various load conditions.

Evaluation of digital metering methods used in protection and reactive power compensation of micro-grids

During the last several decades, energy supply systems (electric/gas/heat) have been threatened by the increasing and destructive natural disasters. When disasters happen, the system infrastructure implementations of the energy supply system are destroyed, then result in the disconnecting of various load consumers. Thus, how to improve energy supply system resilience under disasters is a key problem. In present paper, a four-fold integrated framework is proposed to resist to the natural disasters, which is utilizing mobile hydrogen trucks (MHT) distributing hydrogen tanks to microgrids (MG) based on transportation network (TN). We Firstly, a temporal-spatial natural disaster destructive model is presented, which is deployed to evaluate the destructive level of the electric/heat/gas grid supply networks. Secondly, MHT based multiple travelling model is presented to characterize truck delivery behaviors among hydrogen produce companies and MG, and the travelling time is calculated based on the traffic flow assignment. Next, the operation model of the multi-energy supply (MES) grid-connected MG is presented. Last, based on the survival ability of the grid supply networks, and the arrivals of hydrogen trucks, MES MG runs the optimal operation algorithm. The simulation results reveal that the utilization of MHT efficiently transport hydrogen tanks to MES MGs and can improve the system resilience.

Summary The wide use of renewable energy sources stimulates the implementation of microgrid (MG) systems. MG is an independent power system that consists of various distributed generations (DGs), storage devices, and loads. The optimization problems of MG have gained wide spread interest considering the economic and environmental operation management of the system. To address the multi-variable non-linear programming problem with multiple hybrid constraints, this paper proposes a novel hybrid multi-objective fireworks algorithm with gravitational search operator (MFAGSO). In the algorithm, the operator, as a new search mechanism, directs the sparks in the clustered region to exchange positional information with Pareto-optimal solutions at each iteration process for obtaining accurate results. The operation management of typical grid-connected MG, which comprises various DGs and storage battery, was optimized within 24 hours to evaluate the performance of the proposed algorithm. Considering operation cost and pollutant emission individually or simultaneously, 3 scenarios were involved in operation management for power market planning and practical considerations. The simulation results of the proposed algorithm were compared with those from other methods employed in previous literature and original fireworks algorithm. The results demonstrated the superior performance of the proposed method to solve operation management for optimizing problems of MG.

Multi-objective optimal scheduling of a microgrid with uncertainties of renewable power generation considering user satisfaction

Analyzing flexibility options for microgrid management from economical operational and environmental perspectives

Renewable energy sources are widely acknowledged as the optimal substitutes for conventional energy outlets globally. Countries are endowed with diverse renewable resources, including solar, wind, biomass, hydro, and tidal energy. Despite this abundance, there exists a substantial disparity between the demand and supply of electrical energy, with numerous regions still facing insufficient access to power. The integration of various renewable energy sources in remote and isolated locations forms a Microgrid (MG), catering adequately to local energy requirements. These microgrids have the capability to function seamlessly alongside conventional grids. Hybrid MG system, incorporating Photovoltaic (PV) with battery storage and a Wind Turbine (WT), emerges as a practical solution for electrifying remote areas in islanded mode. The WT’s installed capacity is chosen to meet critical load requirements during periods of non-availability of renewable sources. The battery's capacity is selected to cover the transition period between renewable sources or as a backup source, ensuring cost-effectiveness over WTs. The application of Adaptive Particle Swarm Optimization (APSO) has demonstrated favorable outcomes compared to existing PSO algorithms, enhancing control efficiency. In a grid-connected MG system, the integration of distributed PV generation has notably improved the voltage profile, reducing overall losses. Consequently, the presented MG systems provide both technically and economically viable solutions for ensuringcontinuous electricity supply in isolated and grid-connected systems. This approach not only mitigates pollution but also allows for the possibility of capacity addition, fostering sustainable growth in these regions.

This paper models and solves the operation management problem of MicroGrids (MGs) including cost and emissions minimization under uncertain environment. The proposed model emphasizes on fuel cells (FCs) as a prime mover of combined heat and power (CHP) systems. An electro‐chemical model of the proton exchange membrane fuel cell (PEMFC) is used and linked to the daily operating cost and emissions of the MGs. A reformer is considered to produce hydrogen for PEMFCs. Moreover, in high thermal load intervals, in order to make the MG more efficient, a part of produced hydrogen is stored in a hydrogen tank. The stored hydrogen can be reused by PEMFCs to generate electricity or be sold to other hydrogen consumers. A probabilistic optimization algorithm is devised which consists of 2m + 1 point estimate method to handle the uncertainty in input random variables (IRVs) and a multi‐objective Self‐adaptive Bee Swarm Optimization (SBSO) algorithm to minimize the cost and emissions simultaneously. Several techniques are proposed in the SBSO algorithm to make it a powerful black‐box optimization tool. The efficiency of the proposed approach is verified on a typical grid‐connected MG with several distributed energy sources.