Microgrid Energy Research Articles

RETRACTED ARTICLE: Optimizing residential DC microgrid energy management system using artificial intelligence

RETRACTED ARTICLE: Optimizing residential DC microgrid energy management system using artificial intelligence

A robust optimization model for microgrid considering hybrid renewable energy sources under uncertainties.

Hybrid renewable energy sources and microgrids will determine future electricity generation and supply. Therefore, evaluating the uncertain intermittent output power is essential to building long-term sustainable and reliable microgrid operations to fulfill the growing energy demands. To address this, we proposed a robust mixed-integer linear programming model for the microgrid to minimize the day-ahead cost. To validate the proposed model piecewise linear curve is to deal with uncertainties of wind turbine, photovoltaic, and electrical load. The proposed solution is demonstrated through a case study compared under a robust worst-case scenario, deterministic model, and max-min robust optimization that aim to find optimal robustness. So, a piecewise linear curve is considered to obtain uncertain parameters in order to deal with uncertainties and predict the day-ahead cost. This study illustrates how the Uncertainty Budget Set selection used to integrate renewable energy sources into a microgrid, which manages the energy system. Therefore, the model complexity was slightly modified by adjusting the Uncertainty Budget Set to obtain the optimal decision and control the load demand and uncertainty of renewable energy sources. The comparative results demonstrate that the proposed robust optimization can achieve high solutions under microgrid's availability and is intended to confirm that the proposed method is more cost-effective than alternative optimization techniques. Additionally, the effectiveness and advantage of the proposed methodology in the IEEE 33-node system are validated in this case study by comparing it to the existing optimization. The comparison results show that the proposed robust optimization methods illustrate the model's efficiency, concluding remarks, and managerial insights of the research.

Energy and Economic Analysis of Renewable Energy-Based Isolated Microgrids with AGM and Lithium Battery Energy Storage: Case Study Bigene, Guinea-Bissau

By the year 2020, 90% of the population with access to electricity worldwide was surpassed. However, the reality is very different for many countries, especially for those on the African continent that had more than 572 million people without electricity service at the end of 2019. This work studies the implementation of an isolated microgrid activated with photovoltaic energy and energy storage in batteries under the case study of the community of Bigene, located in the African country of Guinea-Bissau. This type of project is a potential solution to the problem of access to energy, but as the cost of the energy storage system is typically very high, this work technically and economically addresses the effect of using absorbed glass material (AGM) and lithium batteries. A simulator was developed using TRNSYS software to analyze the operation of the microgrid under a defined annual demand profile for different types of users, and economic analysis was conducted considering a project lifetime of 25 years. The results showed no significant differences in the solar fraction of both types of batteries when the photovoltaic power was less than 600 kW, regardless of the capacity of the storage bank. The analysis of auxiliary power requirements showed that lithium technology leads to a lower consumption from 800 kW of PV capacity, and utilizing less than this capacity did not have a significant difference with AGM batteries. In this microgrid with a photovoltaic capacity of less than 700 kW and an energy storage of less than 2580 kWh, the type of storage technology, AGM or lithium, did not represent a considerable difference in the levelized cost of energy, indicating that AGM technology could be selected considering its low initial investment cost compared to lithium batteries.

Optimal Allocation of Energy Storage Capacity in Microgrids Considering the Uncertainty of Renewable Energy Generation

The high dimensionality and uncertainty of renewable energy generation restrict the ability of the microgrid to consume renewable energy. Therefore, it is necessary to fully consider the renewable energy generation of each day and time period in a long dispatching period during the deployment of energy storage in the microgrid. To this end, a typical multi-day scenario set is used as the simulation operation scenario, and an optimal allocation method of microgrid energy storage capacity considering the uncertainty of renewable energy generation is designed. Firstly, the historical scenarios are clustered into K types of daily state types using the K-means algorithm, and the corresponding probability distribution is obtained. Secondly, the Latin hypercube sampling method is used to obtain the state type of each day in a multi-day scenario set. Then, the daily scenario generation method based on conditional generative adversarial networks is used to generate a multi-day scenario set, combining the day state type as a condition, and then the typical scenario set is obtained using scenario reduction. Furthermore, a double-layer optimization allocation model for the energy storage capacity of microgrids is constructed, in which the upper layer optimizes the energy storage allocation capacity and the lower layer optimizes the operation plans of microgrids in each typical scenario. Finally, the proposed model is solved using the PSO algorithm nested with the CPLEX solver. In the microgrid example, the proposed method reduces the expected annual total cost by 19.66% compared with the stochastic optimal allocation method that assumes the scenic power obeys a specific distribution, proving that it can better cope with the uncertainty of renewable energy generation. At the same time, the expected annual total cost is reduced by 6.99% compared with the optimal allocation method that generates typical daily scenarios based on generative adversarial networks, which proves that it can better cope with the high dimensionality of renewable energy generation.

Optimal Scheduling Strategy for Multi-Energy Microgrid Considering Integrated Demand Response

Research on energy storage plants has gained significant interest due to the coupled dispatch of new energy generation, energy storage plants, and demand-side response. While virtual power plant research is prevalent, there is comparatively less focus on integrated energy virtual plant station research. This study aims to contribute to the integrated energy virtual plant station research by exploring the relationship between the integrated energy electro-thermal coupling capacity, various forms of electro-thermal integrated energy response, and electro-thermal integrated energy storage. Analyzing the attributes of an integrated energy microgrid, including energy storage characteristics, time-of-use tariffs, and electric and thermal loads, is crucial. A grid-connected microgrid with cogeneration systems, electric boilers, fuel cells, and energy storage systems is used as an illustrative example. The dispatching method prioritizes multiple complementary energy sources while considering the integrated energy demand response. The study presents different models for the electricity demand and thermal energy demand response and introduces the design of a wholesale power trader involved in building energy storage facilities and participating in the demand response. To verify the feasibility and rationality of the integrated energy demand response scenario, three different schemes are compared, and an economic analysis is conducted.

Optimal design and sizing of renewable energies in microgrids based on financial considerations a case study of Biskra, Algeria

Integration of renewable energy sources in microgrids is a challenging process, where a wealth of metrics should be optimized together to achieve an optimal design. This paper presents a comprehensive approach for selecting the best microgrid structure including a versatile renewable energy source (RES), the proposed microgrid systems are considered using investment costs evaluation and total power generation needs. Through the analysis of the economic feasibility and guiding decision-making on microgrid projects, three system configurations have been examined at the Algerian site of Biskra using the HOMER software. In this work, photovoltaics, photovoltaics/wind, and photovoltaics/wind/diesel projects were explored in respect to the specificities of the targeted region. The Net Present Cost (NPC) was found to be 11.7 M$,13.3 M$, and 9.45 M$ respectively, likewise, Levelized Cost of Energy (LCOE) were 0.19 $/kWh, 0.728 $/kWh, and 0.188 $/kWh respectively. Where a comparative analysis was carried out as a sensitivity analysis to find out the effect of different economic and technical conditions on costs and parameters. Cash flows were also forecasted, and financial criteria such as the Internal Rate of Return (IRR) (13%, 15%), Net Present Value (NPV) (1 M$ to 10 M$) for three projects, and Discounted Payback (DPb) (6 to 9 years). In addition, Monte Carlo simulation is used to assess the risks associated with the project's net present value, this is done to maintain the robustness of project decision-making outcomes. The findings emphasise the importance of financial analysis in the design and sizing of RES microgrid.

Green supply chain management: a renewable energy planning and dynamic inventory operations for perishable products

The emergence of renewable energy microgrids (REM)-supported logistics parks reflects the urgent social requirement for sustainability facing energy crisis and climate change. Focusing on the emergency REM capacity planning and dynamic inventory operations problem for a REM-supported cold warehouse, this paper constructs a novel two-stage stochastic optimization method to comprehensively minimise long-term investment and short-term emergency operation costs of the system, while improving the resilience of energy-constrained supply chain management of perishable products. In particular, we formulate the second stage emergency operation problem as a Markov decision process that considers the interaction between energy and operational decisions constrained by intermittent of renewable energy, the randomness of the occurrence time, and the balance between power supply and temperature changes of the cold warehouse. We propose a novel two-phase PSO-based MPC algorithm to solve the entire problem. We also examine the effectiveness of our algorithm via an empirical study based on the data in East China. Nine typical scenarios clustered from the real data suggest different optimal capacity planning and operational policies. The empirical results show that it can reduce an overall 9% of operating costs on average compared to other heuristic policies. Moreover, installing REM can improve the resilience of power systems by 23% (risk impact area reduction ratio) and improve the resilience of inventory systems by 49%.

Weighting Factor Design Techniques for Predictive Control of Power Electronics and Motor Drives

The rapid growth of energy demand requires progressive energy generation. This, together with the demand for higher efficiency and flexibility, has promoted the application of power electronics in energy systems. During the past decade, model predictive control (MPC) of power electronics has witnessed significant advancements in both dynamic performance and optimal control of the multi-objective terms. Several of these terms can have equal control priorities, resulting in a symmetrical cost function; however, most objectives have different priorities and require weighting factors to resolve the asymmetry in the cost function. Currently, researchers continue to encounter challenges in the optimal design of weighting factors. Moreover, the relative performance of different techniques that either utilize or avoid the weighting factor are uncertain. Therefore, this study focuses on weighting factor design techniques in the literature as applied to wind/solar energy conversion, microgrids, grid-connected converters, and other high-performance converter-based systems. These are grouped under the heuristic, offline tuning, sequential, and online optimization methods. This study demonstrates that optimal online tuning of weighting factors and sequential MPC methods can both offer improved robustness against parameter uncertainties. In addition, the advantages and limitations of different techniques are highlighted.

Reliability-constrained Bi-level collaborative planning of distribution network and micro-energy grid

In order to make full use of the potential reliability value of micro-energy grid (MEG) to defer further grid investment and reduce the energy supply cost, this paper proposes a bi-level collaborative planning model for distribution network and micro-energy grid considering network reliability. With consideration of reliability difference, price signals are used to guide the optimal balance between supply and demand. The upper-level aims at maximizing the profit of the distribution network while optimizing the pricing and expansion planning strategies of the distribution network. And the lower-level aims to minimize the cost of micro-energy grid as well as optimizing the planning and operation strategies of micro-energy grid. A 14-node distribution network system is utilized to conduct the simulation, the results show that the proposed method can improve the economic benefits of distribution network and micro-energy grid and reduce the overall energy supply cost on the premise of meeting users' demands. In addition, with the increase of users' reliability requirements, the effect of reducing the overall energy supply cost is more significant.© 2017 Elsevier Inc. All rights reserved.

Simulation Model and Performance Analysis for Distributed Compressed Air Energy Storage under Off-design Conditions

The dual carbon target in China, which is set to improve the low carbon and economy of regional microgrid (villages in northwest China for example) energy consumption, is proposed to use compressed air energy storage to improve the utilization rate of distributed new energy and reduce the cost of power purchase to the upper power grid. Therefore, the system dynamic characteristics simulation and performance analysis under wide working conditions are very important for improving the utilization rate of new energy. Firstly, the operation characteristics of the compression subsystem and turbine subsystem under wide working conditions are analyzed. Secondly, the mathematical models of the compression subsystem, turbine subsystem, throttle valve, and air storage chamber in the distributed compressed air energy storage system are established. Finally, the dynamic characteristics of energy storage and energy release under different working conditions of the system are studied through simulation. The simulation results show that under the condition of the high mass flow rate, the pressure of the air storage chamber can be increased by 8.29 MPa, and the temperature can be increased by 82 °C. The research results provide a theoretical basis for the system design of distributed compressed air energy storage.

Modeling and optimization of hybrid microgrid energy system: a case study of University of Abuja, Nigeria

This research work modelled and optimized the hybrid microgrid energy system for electricity generation at the University of Abuja, Nigeria, using PV, wind, diesel, and battery renewable energy resources. The model and optimization of the system are performed through HOMER software. The estimated university average annual power consumption is 2355 kWh/day, and the optimal load demand is 313.40 kWp. The PV/wind/diesel/ converter/battery hybrid system has the lowest cost of energy (COE) of 0.1616 $/kWh, operating cost of $50,592, and net present cost (NPC) of $1,795,026 but diesel/wind/converter/battery hybrid system has highest COE of 0.4242 $/kWh and NPC of $4,710,983. The optimal total electricity generated is 1,272,778 kWh/yr while electricity generated by PV contribute the highest energy of 1,030,485 kWh/yr (81%), whereas diesel generator and wind produced energy of 93,927 kWh/yr (7.38%) and 148,366 kWh/yr (11.7%) respectively. The wind/diesel/converter/battery hybrid system produced carbon dioxide (CO2) of 557,749 kg/yr. The most environmentally friendly is the wind/PV/battery and PV/battery hybrid system without pollutants emissions, but the diesel/wind/battery hybrid system has the highest rate of pollutants emissions. The result shows that PV’s electrical power is extremely high from February to June, which causes a high rate of irradiance within the specified period.

Artificial Intelligence Assisted Energy Optimization and Control Method for Microgrids

<p>As the future direction of power development, microgrids are particularly important for rational and efficient energy management. This article establishes an optimization model with multiple uncertainties as parameters for the microgrid energy system, with the objective function of minimizing operating costs. Then, an improved harmony algorithm was used to solve for the optimal solution of the model parameters. Finally, a microgrid system consisting of wind and thermal power units established in a certain area of Hebei Province was used for solution analysis. After experimental verification, the proposed method in this paper achieved significant improvements in both operational cost reduction and microgrid efficiency.</p> <p> </p>

Resilient Microgrid Energy Management Algorithm Based on Distributed Optimization

This article proposes a fully distributed energy management algorithm for dc microgrids, resilient to different faults. Specifically, we employ distributed model-predictive control to deal with the uncertainty that characterizes the microgrid operation. The optimization problem is solved at each time step through a distributed optimization algorithm, which has three main advantages: 1) agents of the network require a small computational power; 2) local information is not shared among the network nodes, hence preserving a certain level of privacy; and 3) it is suitable for implementation in large-scale systems. The resilience property of the algorithm stems from additional constraints that are enforced in order to store in the system enough energy to sustain the microgrid in the case of utility grid or line fault. Simulation results show that the algorithm is suitable to schedule the operation of agents that are always connected to the microgrid (e.g., loads) as well as agents that may be connected and disconnected (e.g., electric vehicles).

Resiliency Enhancement and Power Quality Optimization of Converter-Based Renewable Energy Microgrids

The converter-based renewable energy microgrids are becoming indispensable parts of power grids. Nevertheless, the increasing risks by different causes deteriorate the resiliency of their grid integration. In this paper, a sensorless combined control method for the grid-connected converters is proposed to enhance the resiliency in different aspects and against multiple risks. Power quality improvement is also considered in the proposed method, along with enhancing resiliency. A single control system is proposed to deal with unbalanced grid conditions, severe load changes, and short circuit faults. The control system has a low dependency on the system parameters. Also, a new reference calculation is proposed to improve the system performance under balanced and unbalanced conditions. It provides stable operation under normal and abnormal conditions such as unbalanced voltages, voltage sag, faults, etc. The operation under the proposed control system is compared with the one under a conventional control system to confirm its performance superiority through simulation and experimental results.

What makes Colombia's indigenous peoples adopt microgrids? Social acceptance and financial constraints in renewable energy diffusion

Approximately 1.9 million Colombians lacked access to clean and reliable energy in 2020 due to residing in rural non-interconnected zones (NIZ). This lack of reliable electricity is compounded by their geographical isolation which makes them unable to access the National Interconnected System of energy (SIN). Renewable energy microgrids serve as a potential decentralised solution to provide clean energy to these rural hard-to-reach areas. This study analyses the enablers and barriers to diffusing such renewable energy systems from the views of the rural indigenous Wayuu people in La Guajira, Colombia. Drawing on semi-structured interviews, focus group discussions, and observatory research, the strong relative advantage of the innovations, primarily by making accessing water easier, their high combability with current needs and cultural norms, and low complexity creates a high willingness to adopt the microgrids. The close communal ties and communication between different households allow knowledge about the microgrids benefits to spread which further encourage its diffusion. Still, due to financial constraints, diffusion rates are low. Therefore, with national plans to expand renewable energy development in La Guajira and with legislations supporting clean energy to Colombia's NIZs', the Colombian government and relevant development agencies should support the Wayuu people with finances to enable renewable energy microgrid diffusion.

Expected value model of microgrid economic dispatching considering wind power uncertainty

New energy microgrid is one of the development trends of the future power grid. As there are wind turbines and other new energy equipment in the microgrid, its output is easily affected by unstable weather factors. Moreover, the complexity of dispatching caused by the storage battery in the microgrid and the non-linear factors of the diesel generator have brought great challenges to the economic dispatching of the microgrid. To tackle these problems, this paper establishes the expected value model of microgrid economic dispatching considering the uncertainty of wind power generation and the factors such as battery and diesel generator and proves that this expected value model is convex. On this basis, the piecewise linearization method is proposed to find the global optimal solution of the economic dispatch. Finally, a representative microgrid is optimized to verify the effectiveness of expected value model.

Multi-agent based coalition formation of prosumers in microgrids using the i* goal modelling

In this paper, we discuss the role of microgrids as a “prosumer”. Microgrids are used to provide locally generated power (energy), and this concept is becoming increasingly prominent with time. Microgrids have added economic value when assuming the role of “prosumer” or “group of prosumers”. A new outlook in managing prosumers connected to the energy sharing network has led to the creation of prosumer coalition groups, which can subsequently manage numerous goals in microgrid energy systems. For achieving prosumer energy goals, Goal-Oriented Requirements Engineering (GORE) is deployed in this work. Hence, the purpose of this research is to develop prosumer coalition-GORE artefacts, strategising GORE players, modelling non-functional requirements and ensuring sustainable requirements engineering management in the microgrid energy system. In this research, an i* goal model has been used to design a payoff function based on the game theory concept. The key to the pricing function is its fair distribution of payoffs depending on their surplus energy generation, thus providing optimum satisfaction to the buyer. With the objective of maximising the profits earned by prosumers through intra-microgrid energy trading, this paper also designs multi-objective functions to provide optimal value by using the i* goal model. By integrating Java with the IBM CPLEX optimisation tool, a simulation model based on the proposed method was developed and analysed. The results show that the proposed approach yields better outcomes when meeting the requirements of fairness and efficiency, reducing the intermittency effect of generation through renewable resources.

Microgrid energy scheduling under uncertain extreme weather: Adaptation from parallelized reinforcement learning agents

Microgrids are useful solutions for integrating renewable energy resources and providing seamless green electricity to minimize carbon footprint. In recent years, extreme weather events happened often worldwide and caused significant economic and societal losses. Such events bring uncertainties to the microgrid energy scheduling problems and increase the challenges of microgrid operation. Traditional optimization approaches suffer from the inaccuracy of the uncertain microgrid model and the unseen events. Existing reinforcement learning (RL) - based approaches are also hampered by the limited generalization and the increasing computational burden when stochastic formulations are required to accommodate the uncertainties. This paper proposes a new parallelized reinforcement learning (PRL) method based on the probabilistic events to handle the microgrid energy uncertainties. Specifically, several local learning agents are employed to interact with pertinent microgrid environments in a distributed manner and report outcomes to the global agent, which will optimize microgrid energy resources online during extreme events. The stochastic microgrid energy optimization problem is reformulated to include all possible scenarios with probabilities. The advantage estimate functions of learning agents are designed with a backward sweep to transfer the outcomes to the value function updating process. Two simulation studies, stochastic optimization and online testing, are performed to compare with several existing RL approaches. Results substantiate that the proposed PRL method can achieve up to 20% optimization performance improvement with 4 and 28 times less computation cost than Q-learning with experience replay and multi-agent Q-learning approaches, respectively.

Economic analysis based on saline water treatment using renewable energy system and microgrid architecture

Abstract Reverse osmosis desalination facilities operating on microgrids (MGs) powered by renewable energy are becoming more significant. A leader-follower structured optimization method underlies the suggested algorithm. The desalination plant is divided into components, each of which can be operated separately as needed. MGs are becoming an important part of smart grids, which incorporate distributed renewable energy sources (RESs), energy storage devices, and load control strategies. This research proposes novel techniques in economic saline water treatment based on MG architecture integrated with a renewable energy systems. This study offers an optimization framework to simultaneously optimize saline as well as freshwater water sources, decentralized renewable and conventional energy sources to operate water-energy systems economically and efficiently. The radial Boltzmann basis machine is used to analyse the salinity of water. Data on water salinity were used to conduct the experimental analysis, which was evaluated for accuracy, precision, recall, and specificity as well as computational cost and kappa coefficient. The proposed method achieved 88% accuracy, 65% precision, 59% recall, 65% specificity, 59% computational cost, and 51% kappa coefficient.

A multi-objective optimization model for sizing an off-grid hybrid energy microgrid with optimal dispatching of a diesel generator

The paper presents a multi-objective optimization model for sizing and operating a hybrid energy system consisting of solar photovoltaic, wind energy, diesel generator, and battery storage. A new concept of optimizing the diesel generator loading percentage has been introduced. Also, the introduced battery management criteria will enable an adaptive operation hours of the diesel generator while regulating the battery state of charge. The proposed optimization model aims to minimize the total investment cost, operation & maintenance cost, and system’s carbon emissions. The number of PV panels, number of wind turbines, rated diesel generator capacity, rated battery capacity, and optimal loading percentage of the diesel generator will be optimized. The multi-objective optimization will be done using the Genetic Algorithm using MATLAB software. Obtained results are presented in a Pareto optimal chart for better analysis. The fuel cost per day, the levelized cost of electricity, the diesel generator on hours, and the emission factor of the hybrid system have been determined for each optimal output. A comparison was carried out with a previously proposed study and found that the proposed method increases the lifetime of the diesel generator by 42.9%. The optimal loading percentage of the diesel generator is found to be 75%. The proposed optimization model reduced the total cost and carbon emissions of the hybrid energy system by 3.5% and 0.7%, respectively. The levelized cost of energy and the payback period of the proposed hybrid microgrid system are 0.16 USD/kWh and 8 years, respectively, which verify that the proposed system is economically feasible.