•Overview of microgrid architecture and energy management systems.•Microgrids communication technologies comparative analysis.•Critical review of microgrid energy management system models and ...solution methods.
Renewable energy resources are currently being deployed on a large scale to meet the requirements of increased energy demand, mitigate the environmental pollutants, and achieve socio-economic benefits for sustainable development. The integration of such distributed energy sources into utility grid paves the way for microgrids. The microgrid concept is introduced to have a self-sustained system consisting of distributed energy resources that can operate in an islanded mode during grid failures. In microgrid, an energy management system is essential for optimal use of these distributed energy resources in intelligent, secure, reliable, and coordinated ways. Therefore, this review paper presents a comparative and critical analysis on decision making strategies and their solution methods for microgrid energy management systems. To manage the volatility and intermittency of renewable energy resources and load demand, various uncertainty quantification methods are summarized. A comparative analysis on communication technologies is also discussed for cost-effective implementation of microgrid energy management systems. Finally, insights into future directions and real world applications are provided.
The integration of renewable energy resources (RER) into an existing distribution system is an important topic in dealing with energy challenge the world is facing. With rapid development of electric ...energy storage (EES) technologies, there is a growing interest in integrating both EES and RER into power systems to improve their reliability and economy. In this paper, the adequacy and economy of distribution systems integrated with both EES and RER are assessed. A novel model predictive control (MPC)-based operation strategy is presented to minimize distribution system energy purchasing cost by coordinating multiple power supplies from EES, RER and external grid. An islanding operation is implemented to improve the distribution system reliability and reduce customer interruption cost. A reliability and economy assessment framework based on sequential Monte Carlo method integrated with the MPC-based operation and islanding operation is proposed. Case studies are conducted to demonstrate the reliability and economy improvement by implementing the proposed operation strategies together with integration of EES and RER, and also investigate how EES capacity, power limit, and wind turbine generation capacity affect system reliability and economy.
This paper presents a stochastic planning algorithm to plan an operation of a multi-microgrid (MMG) in an electricity market considering the integration of stochastic renewable energy resources ...(RERs). The proposed planning algorithm investigates the optimal operation of resources (i.e., wind turbine (WT), fuel cell (FC), Electrolyzer, photovoltaic (PV) panel, and microturbine (MT)) and energy storage (ES). Various uncertainties (e.g., the power production of WT, the power production of PV, the departure time of electric vehicle (EV), the arrival time of EV, and the traveled distance of EV) are initially forecasted according to the observed data. The prediction error is estimated by fitting the forecasted data and observed data using a Copula method. A Cournot equilibrium and game theory (GT) are applied to model the real-time electricity market and its interactions with the MMG. The proposed algorithm is examined in a sample MMG to determine the operation of uncertain resources and ES. The obtained results are compared with a baseline and the other conventional optimization methods to verify the effectiveness of the proposed algorithm. The obtained results authenticate the importance of modeling the interaction between the MMG and electricity market, especially under the high integration of uncertain RERs, resulting in above 8% cost reduction in the MMG.
•This paper presents a planning algorithm to address the participation of smart microgrids in an electricity market.•The proposed planning algorithm aims to minimize the total cost, while the optimal sizes of renewable energy resources.•Various uncertainties (renewable power production and electric vehicle specifics) are modeled using Copula method.•The microgrid participates in the electricity market as a price maker and a game theory model is applied.
•Addressing the energy hubs planning and operation problem in a MILP model.•Representation of planning and operation problem by Benders Decomposition approach.•Addressing the stochastic programming ...in the planning and operation problem.•Assessment the effects of demand response programs in the integrated model.
In this paper, an integrated approach for optimal planning and operation of energy hubs is provided considering the effects of wind energy resources. Inevitable uncertainties of electrical, heating, cooling demands as well as the wind power generation are considered in this study. The proposed model is based on two-stage optimization problems and represented as a stochastic programming problem to address the effects of uncertain parameters. In order to address the uncertain parameters in the model, different scenarios have been generated by Monte-Carlo Simulation approach and then the scenarios are reduced by applying K-means method. In addition, the effects of demand response programs on the operational sub-problem are taken into account. Benders decomposing approach is adopted in this research to solve the complex model of coordinated planning and operation problem. The master problem is supposed to determine the type and capacity of hub equipment, while the operating points of these assets are the decision variables of the operational slave problem. As a result, the proposed mathematical model is expressed as a linear model solved in GAMS. The simulation results confirm that the Benders decomposition method offers extremely high levels of accuracy and power in solving this problem in the presence of uncertainties and numerous decision variables. Moreover, the convergence time is drastically decreased using Benders decomposition method.
Advanced energy storage has been a key enabling technology for the portable electronics explosion. The lithium and Ni-MeH battery technologies are less than 40 years old and have taken over the ...electronics industry and are on the same track for the transportation industry and the utility grid. In this review, energy storage from the gigawatt pumped hydro systems to the smallest watt-hour battery are discussed, and the future directions predicted. If renewable energy, or even lower cost energy, is to become prevalent energy storage is a critical component in reducing peak power demands and the intermittent nature of solar and wind power. An electric economy will demand more electrification of the transportation sector and it is likely that all vehicles sold by the end of this decade will have some level of hybridization. Energy storage capabilities in conjunction with the smart grid are expected to see a massive leap forward over the next 25 years.
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•Pearson correlation is most common metric of complementarity quantification.•Concept of complementarity is often mentioned but clear application is not provided.•Most research ...activities focus on: Brazil, Europe, China and USA.
Global and regional trends indicate that energy demand will soon be covered by a widespread deployment of renewable energy sources. However, the weather and climate driven energy sources are characterized by a significant spatial and temporal variability. One of the commonly mentioned solutions to overcome the mismatch between demand and supply provided by renewable generation is a hybridization of two or more energy sources into a single power station (like wind-solar, solar-hydro or solar-wind-hydro). The operation of hybrid energy sources is based on the complementary nature of renewable sources. Considering the growing importance of such systems and increasing number of research activities in this area this paper presents a comprehensive review of studies which investigated, analyzed, quantified and utilized the effect of temporal, spatial and spatiotemporal complementarity between renewable energy sources. The review starts with a brief overview of available research papers, formulates detailed definition of major concepts, summarizes current research directions and ends with prospective future research activities. The review provides a chronological and spatial information with regard to the studies on the complementarity concept.
Energy demand in Pakistan has increased surprisingly for a few years. The current generation of Pakistan is mainly reliant on thermal (fossil fuels) and crude oils. Pakistan is facing an energy gap ...of 5000-6000 MW between the demand and supply chain. The difference in Energy demand and energy generation for Pakistan is major causes of power system blackouts, power tripping, and load shedding problem. This entity intrigues the government of Pakistan to open the door for Alternative Energy Sources (AES) i.e. biogas, wind and solar, etc. Biogas becomes a vital source of energy because of technological evolution, free source, environmental friendly, consumer insistence and different state policies. Integration of biogas generation through Net Metering (N-M) with electricity grid at different levels (domestic, commercial and industrial level) can minimize the energy deficiency. Present research investigates the biogas Net Metering in Pakistan. Analysis has been performed through ETAP software. It is observed that the primary burdened bus 21 has a 98.76% voltage level in Case 2, while in Case 1 the voltage profile of Bus 21 is 97.78%. Results show the contribution of Distributed Generation (DG) system in the reduction of power deficit and burden on the utility grid.
Utilizing renewable energy resources is one of the convenient ways to reduce greenhouse gas emissions. However, the intermittent nature of these resources has led to stochastic characteristics in the ...generation and load balancing of the microgrid systems. To handle these issues, an energy management optimization for microgrids operation should be done to urge the minimization of total system costs, emissions, and fuel consumption. An optimization program for decreasing the operational cost of a hybrid microgrid consisting of photovoltaic array, wind unit, electrolyzer, hydrogen storage system, reformer, and fuel cell is presented. Two different methods of producing hydrogen are considered in this study to ensure the effectiveness of the developed methodology. In the microgrid system with high penetration of renewable energy resources, using storage technologies to compensate for the intermittency of these resources is necessary. To evaluate the functioning of the microgrid system, a mathematical model for each source is developed to coordinate the system operation involving energy conversion between hydrogen and electricity. Particle Swarm Optimization Algorithm is utilized to determine the optimum size and operational energy management within the system. It is evident from the results that there is about a 10% reduction in the amount of CH4 consumption in reformer when the electrolyzer was employed in the system. It is observed that the CH4 reduction in summer and fall is higher than other seasons (10.6% and 11.5%, respectively). The reason is that the highest RES production occurs in these seasons during a year. It is also worth mentioning that the electrolyzer technology would play a significant role in decreasing the CH4 consumption in the microgrid system.
•Using fuel cells with renewable energy-based DER to minimize the total system cost.•Considering efficient various hydrogen production to produce hydrogen.•Utilizing electrolyzer to utilize surplus power of renewable energy resources.•Generating hydrogen by electrolyzer reduce the fuel consumption in fuel cells.•Utilizing hydrogen storage brings more financial benefits to the system.
Resource strategy concerns and the need for mitigation of environmental impacts associated with energy generation from fossil fuels have increased the deployment of renewable energy carriers such as ...biogas. Biogas has beneficial environmental aspects such as waste treatment, production of energy from waste and general substrates and a better way to spread the fermented residues through improved nutrient and flow abilities. The objective of this study was to assess the status of biogas production and its effects on the environment due to greenhouse gases (GHG) and other environmental impacts. In this work, 15 life cycle assessment (LCA) studies of biogas systems from around Europe were reviewed. Biogas scenarios in all the studies had lower GHG intensities than their reference systems.
The study shows that the type of feedstock, e.g., maize, grass or animal manure is a determining factor for the environmental impacts of biogas systems. Improving biogas plant technology and management by collecting the biogas during the storage of digested residues or installing a gas flare will improve the greenhouse gas balance of biogas systems. In comparison with traditional manure storage, anaerobic digestion of animal manure avoids methane (CH4) and nitrous oxide (N2O) emissions and adds to the substitution of artificial mineral fertilizer. Apart from the impacts resulting from the production of energy crops, acidification (AC) and eutrophication (EP) from biogas systems could be reduced by using combined heat and power units (CHPU) with catalytic converters and high efficiency.
Power prediction is now a crucial part of contemporary energy management systems, which is important for the organization and administration of renewable resources. Solar and wind powers are highly ...dependent upon environmental factors, such as wind speed, temperature, and humidity, making the forecasting problem extremely difficult. The suggested composite model incorporates Long Short-Term Memory (LSTM) and Swarm Intelligence (SI) optimization algorithms to produce a framework that can precisely estimate offshore wind output in the short term, addressing the discrepancies and limits of conventional estimation methods. The Coati optimization algorithm enhances the hyper parameters CNN-LSTM. Optimum hyper parameters improvise learning rate and performance. The day-ahead and hour-ahead short-term predictions RMSE can be decreased by 0.5% and 5.8%, respectively. Compared to GWO-CNN-LSTM, LSTM, CNN, and PSO-CNN-LSTM models, the proposed technique achieves an nMAE of 4.6%, RE 27% and nRMSE of 6.2%. COA-CNN-LSTM outperforms existing techniques in terms of the Granger causality test and Nash-Sutcliffe metric analysis for time series forecasting performance, scores are 0.0992 and 0.98, respectively. Experimental results show precise and definitive wind power-making predictions for the management of renewable energy conversion networks. The presented model contributes positively to the body of knowledge and development of clean energy.
•Propose COA-CNN-LSTM model: Coati Optimization Algorithm-based hybrid deep learning CNN-LSTM.•Solar and wind powers are dependent upon environmental factors, making the forecasting problem very difficult.•COA-CNN-LSTM outperforms other techniques in terms of the Granger causality test and Natch Shitcliff analysis.•Experiments show precise and definitive wind power-making predictions for the management of RES.
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