Renewable Microgrid Research Articles

A novel fuzzy cloud stochastic framework for energy management of renewable microgrids based on maximum deployment of electric vehicles

The high penetration of renewable energy sources in varied types along with the electric vehicles charging demand has created new challenges against the optimal operation and management of these systems. Therefore, this article attempts to formulate, model and operate the renewable microgrids considering different types of renewable sources including solar units, wind units and storages in the presence of electric vehicles. Due to the volatile and random nature of renewable sources and the charging demands by the electric vehicles, it is needed to find a solution for optimal control of these technologies. In this way, a new policy is devised for control of the charging demand in three different schemes, so-called the smart scheme, coordinated scheme and uncoordinated scheme. In order to capture the influence of uncertainties rooting from the renewable sources in the problem, a novel intelligent approach based on fuzzy cloud theory is used which generates an entropy-entropy concept for its task. Through this model, not only the uncertainty of the random variables is handled, but also the uncertainty of the probability density function (PDF) type is also deployed. In order to solve the problem, a swarm optimization based on honeybee mating algorithm (HMA) is proposed which is equipped by the polar search operators for making a symmetrical searching frame. The simulation results on a typical renewable microgrid test system advocate the quality and appropriate efficacy of the model.

Stochastic energy management in a renewable energy-based microgrid considering demand response program

Climate changes increase concerns about global warming caused by greenhouse gases and have also increased the focus and implementation of renewable energy sources (RESs) planning. One of the important RESs is tidal energy or tidal power, which is a form of hydropower that converts the energy obtained from tides into the electrical power. Although tidal power is still not widely used, this energy resource has the potential for the future electricity generation. This paper addresses the stochastic energy management in a microgrid considering RESs such as solar, wind and tidal sources in the presence of the demand response program and storage devices. The uncertainty of the RESs, demand, and electricity price is handled by Monte Carlo simulation (MCS). The model is a linear multi-objective optimization which the first objective aims to reduce the cost and the second aims to reduce the emission. Augmented ε-constraint approach is applied to solve the problem in the CPLEX/GAMS software environment. The interactive fuzzy decision-making is applied to choose the best answer among the Pareto answers according to the planner criteria.

Operation characteristics and key technology analysis of high-penetration renewable energy microgrid

With the increasing penetration rate, the strong intermittency and volatility of renewable energy, and the large-scale use of power electronic devices, the safe and stable operation of microgrids has been seriously affected. This paper analyzed the impact of high-penetration renewable energy on power system flow distribution, power quality, operating status, relay protection, etc. And specifically studied several specific keys to improve the operating stability of the microgrid including high-penetration renewable energy.

A Permissioned Blockchain-Based Energy Management System for Renewable Energy Microgrids

Peer-to-peer (P2P) energy management is one of the most viable solutions to incentivize prosumers in renewable energy microgrids. As the application of blockchain expends from the finance field to energy field, blockchain technology provides a new opportunity for distributed energy systems. However, a distributed energy system based on blockchains allows any node in the whole network to read data. In many application scenarios, user privacy cannot be effectively protected, and there is a security problem that the attack cannot be traced. In this paper, we propose an energy management mode based on a permissioned blockchain for a renewable energy microgrid. The novel permissioned blockchain framework uses entity mapping with a unique identity for each enterprise, natural person, or device, in order to avoid ineligible participants to join the microgrid. Each peer entity keeps the transaction information index of the whole network, but only keeps its own specific transaction information, so they can retrieve the transaction information of other peer entities but cannot obtain the details without permission. Moreover, this model could avoid communication delays and promote plug-and-play due to the distributed nature of the permissioned blockchain. The performance of the proposed method is evaluated with a demonstration program which is designed and deployed on a Hyperledger Fabric permissioned blockchain. Simulation results show the feasibility of the proposed method, and the model is conducive to the protection privacy and P2P energy management for decentralized energy systems.

An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand

Renewable microgrids are new solutions for enhanced security, improved reliability and boosted power quality and operation in power systems. By deploying different sources of renewables such as solar panels and wind units, renewable microgrids can enhance reducing the greenhouse gasses and improve the efficiency. This paper proposes a machine learning based approach for energy management in renewable microgrids considering a reconfigurable structure based on remote switching of tie and sectionalizing. The suggested method considers the advanced support vector machine for modeling and estimating the charging demand of hybrid electric vehicles (HEVs). In order to mitigate the charging effects of HEVs on the system, two different scenarios are deployed; one coordinated and the other one intelligent charging. Due to the complex structure of the problem formulation, a new modified optimization method based on dragonfly is suggested. Moreover, a self-adaptive modification is suggested, which helps the solutions pick the modification method that best fits their situation. Simulation results on an IEEE microgrid test system show its appropriate and efficient quality in both scenarios. According to the prediction results for the total charging demand of the HEVs, the mean absolute percentage error is 0.978, which is very low. Moreover, the results show a 2.5% reduction in the total operation cost of the microgrid in the intelligent charging compared to the coordinated scheme.

Multi-Objective Sizing Optimization of Hybrid Renewable Energy Microgrid in a Stand-Alone Marine Context

Islands are the main platforms for exploration and utilization of marine resources. In this paper, an island hybrid renewable energy microgrid devoted to a stand-alone marine application is established. The specific microgrid is composed of wind turbines, tidal current turbines, and battery storage systems considering the climate resources and precious land resources. A multi-objective sizing optimization method is proposed comprehensively considering the economy, reliability and energy utilization indexes. Three optimization objectives are presented: minimizing the Loss of Power Supply Probability, the Cost of Energy and the Dump Energy Probability. An improved multi-objective grey wolf optimizer based on Halton sequence and social motivation strategy (HSMGWO) is proposed to solve the proposed sizing optimization problem. MATLAB software is utilized to program and simulate the optimization problem of the hybrid energy system. Optimization results confirm that the proposed method and improved algorithm are feasible to optimally size the system, and the energy management strategy effectively matches the requirements of system operation. The proposed HSMGWO shows better convergence and coverage than standard multi-objective grey wolf optimizer (MOGWO) and multi-objective particle swarm optimization (MOPSO) in solving multi-objective sizing problems. Furthermore, the annual operation of the system is simulated, the power generation and economic benefits of each component are analyzed, as well as the sensitivity.

A Novel Renewable Microgrid-Enabled Metro Traction Power System—Concepts, Framework, and Operation Strategy

This article presents a novel renewable microgrid-enabled metro traction power system (MMTPS), where the renewable energies and regenerative braking (RB) power of metro trains are unifiedly managed by renewable microgrids to reduce energy consumption and obtain the economic benefits for metro traction power system (MTPS). The concept and system framework of MMTPS is first established, where the metro energy router (MER) is presented as an innovative solution to efficiently integrate renewable microgrids into MTPS. Then, the four operation modes of MMTPS are defined and analyzed, including the voltage control mode, the power control mode, the RB supporting mode, and the islanded mode. Furthermore, the system average model of the proposed MMTPS is established to investigate the steady-state performances of MMTPS. In addition, the control strategies of MMTPS are developed to enable the different operation modes. Finally, the simulation and hardware-in-the-loop (HIL) experimental results are given to validate the effectiveness of the MMTPS. The proposed MMTPS as an innovative solution is able to reduce the energy consumption of MTPS and obtain economic benefits from renewable energy sources and RB energy.

Hybrid machine learning based energy policy and management in the renewable-based microgrids considering hybrid electric vehicle charging demand

In this paper, a secured energy management architecture for both islanded and grid-connected operation modes of renewable hybrid AC-DC microgrids considering various renewable energy sources, distributed power generation units, energy storage, and plug-in hybrid electric vehicles (PHEV) is proposed. In this architecture, whale optimization algorithm (WOA) is employed to minimize the operation cost of the grid and also an intrusion detection system (IDS) based on the sequential hypothesis testing (SHT) approach is introduced to detect identity-based cyber-attacks (i.e. Sybil attack, masquerading attack) on the wireless-based advanced metering infrastructures (AMI). The proposed IDS makes use of the received signal strength (RSS) value to distinguish different signal sources and detect cyber-attacks. The feasibility and performance of the proposed architecture are tested on a practical hybrid microgrid, which is constructed based on the IEEE 33-bus test system. the results are provided for both islanded and grid-connected operation modes.

Enhanced emperor penguin optimization algorithm for dynamic economic dispatch with renewable energy sources and microgrid

In this paper, an enhanced version of the emperor penguin optimization algorithm is proposed for solving dynamic economic dispatch (DED) problem incorporating renewable energy sources and microgrid. Dynamic economic load dispatch optimally shares the power on an hourly basis for a day among the committed generating units to satisfy the feasible load demand. Emission of pollutants from the combustion fossil fuel and gradual depletion of fossil fuel encourages the usage of renewable energy sources. Implementation of renewable energy sources with the reinforcement of green energy transforms the fossil fuel-based plant into a hybrid generating plant. The increase in power production with the increase in electricity demand implicates challenges for economical operation. The proposed algorithm is applied to the DED problem for fossil fuel based and renewable energy system to find economic schedule of generated power among the committed generating units. The proposed optimization algorithm is inspired by the huddling behavior of the emperor penguin. The exploration strategy is enhanced by adapting oppositional based learning. Chaotic mapping is used to maintain a proper balance between exploration and exploitation in the entire search space, which minimizes the cost of generation in the power system.

Optimal Scheduling of Cooling and Power Combined Supply System for Tropical Renewable Microgrids

To satisfy the residential cooling demand in tropical large renewable microgrids (MGs), multiple ice-storage air conditionings (IACs) are usually used to supply cold energy. However, these IACs could not directly exchange the cold energy, which seriously limits the combined regulation effect. To address the problem, a novel cooling and power combined supply system (CPCSS) is proposed in this paper, where the trucks are used to distribute the ices among IACs. To evaluate the performance on the multi-time scales, a cooling and power combined supply model is established. On this basis, a two-stage (i.e., day-ahead and real-time stage) scheduling strategy is presented to minimize the MG operation cost. To speed up the solution, this scheduling model is first linearized as a classic mixed-integer second-order cone programming (MISCP) problem, and then is solved by Lagrange solution method. Simulation studies on an IEEE 14-node system indicates that compared with the existing MG operation cost, the annual operational cost of the based-two-truck CPCSS could be reduced by 9868800 yuan (12.6%), while the network loss could be decreased from 5.4 to 3.9 MWh. The results confirm the effectiveness of the proposed strategy.

Techno-Economic and Power System Optimization of a Renewable Rich Islanded Microgrid Considering Different Dispatch Strategies

In this work, the evaluation of the design and optimization of proposed offgrid hybrid microgrid systems for different load dispatch strategies is presented by assessing the component sizes, system responses and different cost analyses of the proposed system. This study optimizes the sizing of the Barishal and Chattogram (two popular divisions in Bangladesh) hybrid microgrid systems consisting of wind turbine, storage unit, solar PV, diesel generator and a load profile of 27.31 kW for five dispatch techniques: (i) generator order, (ii) cycle charging, (iii) load following, (iv) HOMER predictive dispatch and (v) combined dispatch strategy. The considered microgrids are optimized for the least CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> gas emission, Net Present Cost, and Levelized Cost of Energy. The two microgrids are analyzed for the five dispatch techniques using HOMER software, and subsequently, the power system performance and feasibility study of the microgrids are performed in MATLAB Simulink. The results in this research provide a guideline to estimate different component sizes and probable costing for the optimal operation of the proposed microgrids under various load dispatch conditions. The simulation results suggest that `Load Following' is the best dispatch strategy for the proposed microgrids having a stable power system response with the lowest net present cost, levelized cost of energy, operating cost, and CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emission rate. Additionally, the combined dispatch strategy is determined to be the worst dispatch technique for the proposed off-grid hybrid microgrid design having the maximum levelized cost of energy, net present cost, operating cost and CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emission.

Techno-Economic Design of a Combined Heat and Power Microgrid for a Remote Community in Newfoundland Canada

Around 300 remote communities in Canada rely on diesel for their energy needs, a situation associated with high costs, high emissions, and accessibility problems. Various studies have addressed this problem by proposing renewable energy microgrids, which have a lot of potential due to the abundance and availability of renewable sources. However, there is a lack of studies regarding remote communities in Newfoundland and combined heat and power microgrids. This study chose Cartwright remote community based on consumption and available resources. Both distributed and centralized microgrid components were designed using MS Excel, Polysun, HOMER, and BEOPT. The final system included solar thermal, PV, wind energy, hydroelectric energy and fuel cells for energy generation, and hydrogen as an energy carrier for storage. The solar thermal distributed system reduced the thermal load by 35%. The microgrid reduced diesel consumption by 71% and CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions by 9000 tons. Renewable sources provided 100% of the electric load and 63.5% of the thermal load. The microgrid achieved a Levelized cost of -0.0245 $ \$ $ /kWh, which is only possible for combined heat and power systems.

Lifetime Estimation of Grid-Connected Battery Storage and Power Electronics Inverter Providing Primary Frequency Regulation

Battery Energy Storage Systems (BESSs) are a new asset for Primary Frequency Regulation (PFR), an ancillary service for improving the grid stability. The system operators determine the implementation and remuneration of PFR. However, assessing the revenue stream is not enough to define the business case, as also the components' lifetime has to be estimated. Previous studies of lifetime estimation for BESSs performing PFR considered only the electrochemical storage, disregarding the power electronics (PE). Nonetheless, researchers have shown the importance of estimating PE wear due to the operation when applied in renewable energy generation and microgrids. This paper presents a lifetime analysis of BESSs providing PFR considering IGBT modules, electrolytic capacitors and electrochemical storage degradation. The lifetime information is used to estimate BESS's Net-Present-Value (NPV), evaluating the benefits of deploying PE-based BESS in the European grid. A comparison between different countries, Germany, the Netherlands, and the U.K., is performed, considering the PFR implementation and remuneration differences. The analysis shows that the BESS management strategy can extend its lifetime and that the component that exhibits the shortest lifetime is the electrochemical storage. The PE components are subject to low wear due to the low power utilization and, therefore, small thermal swings while performing PFR. In conclusion, the provision of PFR by means of BESS has been found to be profitable in all three countries. However, in the Netherlands, the potential NPV has been estimated to be 47% and 76% higher than in Germany and the U.K., respectively.

Improving the Transient Response of Hybrid Energy Storage System for Voltage Stability in DC Microgrids Using an Autonomous Control Strategy

In renewable microgrid systems, energy storage system (ESS) plays an important role, as an energy buffer, to stabilize the system by compensating the demand-generation mismatch. Battery energy storage system serves as a decisive and critical component. However, due to low power density and consequently slow dynamic response the lifetime of BESS is observably reduced due to high current stress, specifically experienced during abrupt/transient power variations. Hence, hybridization with supercapacitor storage system is conferred. Additionally, the controllers designed for energy storage systems should substantially respond for compensating the transient requirement of the system. In this article, we propose a decoupled control strategy for batteries and supercapacitors based on k - Type compensators and a nonlinear PI controller (NPIC) respectively. The formulated control design is tested for voltage regulation in a standalone microgrid. Furthermore, a comparative analysis is presented with benchmark low-pass-filter (LPF) based controller. The results obtained shows the proposed control technique possess a faster response with improved voltage regulation capabilities. For the test system regulated at 48 V for various abrupt load-generation various case studies presented, the proposed methodology maintains a significantly reduced voltage deviation between 47 V - 51 V in contrast to 45 V - 56 V observed in the LPF methodology. Furthermore, the complexity is simpler in comparison to LPF based control strategy and a comparative obviation of additional sensing devices is achieved, that inherently reduces the detrimental effect on ESS response during transient condition.

Optimal sizing of Battery and Hydrogen Energy Storage Systems configurations in a Hybrid Renewable Microgrid

Considering the increasing penetration of variable and non-dispatchable renewable energy in worldwide electricity mixes, an increasing requirement for energy storage capacity is foreseen in order to decouple production and demand. Electrochemical battery systems and/or hydrogen systems (electrolysers and fuel cells) provide a suitable alternative to be implemented in local small-to-medium scale microgrid environments. The research aims to address the optimal sizing of an Energy Storage System composed of lead acid batteries and a hydrogen loop (electrolyser, compressed storage tank and fuel cell) within an actual hybrid renewable microgrid located in Huelva, Spain. The energy storage systems must couple the variable production of 15 kWp of solar PV systems and a 3 kWnom horizontal axis wind turbine to a real monitored residential load, which present a time-shifted power demand. By making use of previously developed and validated component models, three storage configurations (battery-only, hydrogen-only and hybrid batteryhydrogen) are assessed via parametrical variation in yearly simulations in hourly timestep, analysing the Loss of Load (LL) and Over Production (OP) output values. The results provide quantitative information regarding the optimal storage system capacity in each configuration providing valuable insight in terms of sizing of the energy storage systems in the long-term.

Dynamic modeling and coordinated multi-energy management for a sustainable biogas-dominated energy hub

Low renewable energy source (RES) accommodation and energy utilization efficiency impose great challenge to reliable energy supplies for rural residents. This paper proposes an optimal coordinated multi-energy conversion and management framework with a biogas-dominated hybrid renewable microgrid for multi-carrier energy supplies in off-grid remote areas. In this framework, multi-energy multi-timeframe couplings among biomass and other renewables are modeled based on biogas digesting thermodynamics for the coordinated interaction among electricity, biogas and thermal energy carriers, and a sustainable energy hub is formulated for mapping various renewables into diversified energy loads. The proposed energy hub can facilitate mitigating the fluctuating outputs of RES by harvesting hydro-wind-solar energies into the form of biogas, and an evaluation model is proposed based on hydrodynamic networking mechanisms to calculate the state of energy (SOE) in the biogas storage. Furthermore, a hierarchical multi-energy management strategy is presented to dynamically optimize the production, conversion, storage and consumption of multi-carrier energy flows for system energy-efficiency enhancement. Case studies on a stand-alone microgrid demonstration validate that the biogas yield can be improved by 31.75% with digesting thermodynamic effects, and the battery degradation cost can be reduced by at least 22.63% considering the SOE of biogas storage with hydrodynamic effects.

Three-level day-ahead optimal scheduling framework considering multi-stakeholders in active distribution networks: Up-to-down approach

Distribution systems are transforming from passive networks to active ones with the development and deployment of renewable energy sources, microgrids (MGs) and virtual plants. Accompanied by the emergence of multiple stakeholders owning personal distributed generators (DGs), energy storage systems and even MGs, the optimal dispatch problem in active distribution networks (ADNs) is challenged. Basing on three-level day-ahead optimal scheduling framework, an up-to-down interaction mechanism is proposed firstly for the optimization in ADNs considering multi-stakeholders. The optimization mechanism starts from DN-layer with minimizing total active power loss of the DN, and then it implements the optimizations of all MGs within the optimizations of all users in the corresponding MG. The optimizing information of connection nodes will be fed back to upper layers in turn and this three-level optimization will be repeatedly implemented until the maximal unbalance power generation returned from all MGs satisfying the convergence condition. Additionally, an elasticity coefficient-based demand response program using time-of-use (TOU) pricing is integrated into the optimizations of MG-layer and User-Layer to guide peak load cutting. Moreover, a power flow rebalancing strategy is also integrated into the MG-layer optimization to accelerate the convergence of the whole solution. Finally, an actual 47-bus distribution system is employed to verify this proposed three-level optimization and the results show the effectiveness and applicability of the proposed method.

Risk assessment of renewable energy-based island microgrid using the HFLTS-cloud model method

Difficulty in power supply has always been the main factor hindering the sustainable development of islands. It is an effective way to address the energy supply issue of off-grid islands by building island microgrid system with abundant renewable energy, such as wind energy and solar energy. However, island microgrids offer a mix of the high penetration rate of renewable energy and the complex island environment, being confronted with many challenges in the whole life cycle. To implement the project smoothly and make full use of the advantages of renewable energy-based island microgrids, a risk assessment must be incorporated. In this study, based on a three-dimensional model, we take a holistic view of identifying risks that includes four categories (technical, economic, environmental and social aspects). Later, a reasonable and applicable risk assessment framework is constructed, in which the fuzziness and randomness of information are taken into consideration. The hesitant fuzzy linguistic term set is used to endow the index evaluation information, and the cloud model is used to transform the evaluation information. The final results show that the overall risk level of China’s island microgrids is “Slightly High”, particularly in the technical aspect. Furthermore, corresponding mitigation strategies are put forward. This study conducts a comprehensive study on the risk assessment and risk response measures of island microgrids, which is conducive to deal with potential risks, thereby to minimize the loss and promote the island microgrid development.

Surrogate optimization of coupled energy sources in a desalination microgrid based on solar PV and wind energy

This paper proposes the use of new software called DesalinationPlant to solve the convoluted issue of calculating nominal capacities for an electric-to-water micro-grid comprised by an inexhaustible distributed energy resources; a decentralized storage system, and; a seawater desalination system. For this task, DesalinationPlant will use global stochastic black-box methods to optimize the amount of distributed resources and decentralized batteries coupled to a water desalination system. The final aim of this optimization is to maximize the profits of renewable micro-grid. DesalinationPlant is able to model very complex engineering systems, obtaining a feasible optimum in a limited time due to the small volume of evaluations needed to come to an optimum result. In this article, a complete desalination system associated with a photovoltaic plant, a wind power plant, electrochemical storage and a connection to the electric network was modeled to optimize the sizing of the distributed renewable resources. The optimization produces useful “optimal” values of the objective function, very “expensive” per se, in less than 100 evaluations. Therefore, it can be concluded that the method used, the DYCORS method is an excellent alternative to other black-box optimization methods such as genetic algorithms.

Probabilistic Load Forecasting Based on Adaptive Online Learning

Load forecasting is crucial for multiple energy management tasks such as scheduling generation capacity, planning supply and demand, and minimizing energy trade costs. Such relevance has increased even more in recent years due to the integration of renewable energies, electric cars, and microgrids. Conventional load forecasting techniques obtain single-value load forecasts by exploiting consumption patterns of past load demand. However, such techniques cannot assess intrinsic uncertainties in load demand, and cannot capture dynamic changes in consumption patterns. To address these problems, this paper presents a method for probabilistic load forecasting based on the adaptive online learning of hidden Markov models. We propose learning and forecasting techniques with theoretical guarantees, and experimentally assess their performance in multiple scenarios. In particular, we develop adaptive online learning techniques that update model parameters recursively, and sequential prediction techniques that obtain probabilistic forecasts using the most recent parameters. The performance of the method is evaluated using multiple datasets corresponding with regions that have different sizes and display assorted time-varying consumption patterns. The results show that the proposed method can significantly improve the performance of existing techniques for a wide range of scenarios.