Microgrid Energy Research Articles

Grid-connected Inverter Control Strategy of New Energy Microgrid

The traditional power system is composed of a synchronous generator. Because the rotor of the synchronous generator has the characteristics of the moment of inertia and damping, it can provide or absorb excess energy when the system’s frequency fluctuates. The inertia and damping of synchronous generators determine the frequency dynamic response process of the power grid, which further affects the operation, control, and protection of the whole power grid. However, because renewable energy is connected to the power grid by power electronic equipment, it does not have mechanical inertia and damping characteristics. With the increase of installed capacity of new energy, the whole power system shows low inertia characteristics. In this case, the power grid is more sensitive to interference, which may further lead to load shedding or system instability, or even system collapse. Virtual synchronous generator technology can solve this problem.

DESIGN OF CLOSED LOOP MULTIPORT DC DC CONVERTER

In recent years, there has been lots of emphasis put on the development of renewable energy. While considerable improvement on renewable energy has been made, there are some inherent limitations for these renewable energies The Closed-Loop Multiport DC-DC Converter with Adaptive Sliding Mode Control (ASMC) is difficult in power electronics system designed to efficiently manage and control energy flow between multiple energy sources and loads in various applications, including renewable energy integration, electric vehicles, and microgrids. This paper provides an overview of the key features and benefits of this innovative converter system. Multiport DC-DC converters have gained significant attention due to their ability to interface multiple energy sources, such as solar panels and batteries with different voltage levels and power ratings, providing a versatile platform for energy management. The ASMC controller enhances the converter's performance by dynamically adjusting the control parameters based on system conditions and load requirements, thereby improving efficiency and reliability .The ASMC employs a sliding mode control strategy, a robust and adaptable control method, which ensures that the converter operates effectively under various operating conditions, including rapid changes in load and source characteristics. This control technique offers inherent advantages, such as fast transient response and reduced sensitivity to parameter variations, contributing to superior system performance. The ASMC controller optimizes power conversion by minimizing switching losses and ensuring that the converter operates close to its peak efficiency. The controller continuously adapts to changes in input voltage, output voltage, and load conditions, ensuring stable and reliable operation even in dynamic environments. The sliding mode control strategy enables rapid response to sudden load changes, making it suitable for applications with varying power demands. Key Words: DC-DC converter, Sliding-mode control, Battery, Solar PV.

Optimization Method of Photovoltaic Microgrid Energy Storage System Based on Price-based DR

Extreme events lead to an increasing number of power outages in the distribution network. It is currently the most effective method to restore power supply after distribution network failure to connect distributed photovoltaic to the distribution network in the form of microgrids. However, the randomness of distributed PV output and load is the biggest obstacle limiting its development. Therefore, an optimization method of photovoltaic microgrid energy storage system (ESS) based on price-based demand response (DR) is proposed in this paper. Firstly, based on the influence of the uncertainty of the time of use (TOU) and load on the price-based DR, a price-based DR model is built. Then, taking the minimum difference between photovoltaic output and load demand as the optimization objective, the optimal operation model of price-based DR based on the fuzzy chance constrained program (FCCP) is established, and an optimization model of photovoltaic microgrid ESS based on FCCP is obtained. Finally, the fuzzy chance optimization problem is converted into a deterministic optimization problem through constraint equivalent treatment. The proposed method is verified by numerical simulations.

Modeling and optimization of PV-diesel-biogas hybrid microgrid energy system for sustainability of electricity in Rural Area

The paper presented a hybrid micro-grid renewable energy of different energy sources to generate uninterrupted electricity for the Agu-Amede village using PV, diesel, biogas, and battery. The system optimization was performed to determine techno-economic analysis and energy generated using HOMER software. The energy generated by the hybrid system was able to meet the demand load of the village at all levels of biomass production with surplus electricity in each case when the production of the biogas system varied between 0.5 tons to 3 tons. At biomass production of 3 tons, the total energy generated demand is 480693 kWh with a surplus of 121762 kWh. Again, the hybrid PV and biogas system generate total energy of 98714 kWh (20.50%) and 381979 kWh (79.50%), respectively. The net present cost (NPC) and cost of energy (COE) of the base case architecture (diesel) measured are $1230000 and $0.267/kWh, respectively. Therefore, between 2.5 tons and 3.0 tons of biomass penetration, the energy production of biogas increased, and the diesel system was removed from the hybrid system setup. This is because the availability of biogas is almost sufficient to meet the load demand at a lower cost and solar PV architecture was added to the hybrid system to balance energy production.

Safe Deep Reinforcement Learning for Microgrid Energy Management in Distribution Networks With Leveraged Spatial–Temporal Perception

Microgrids (MG) have recently attracted great interest as an effective solution to the challenging problem of distributed energy resources’ management in distribution networks. In this context, despite deep reinforcement learning (DRL) constitutes a well-suited model-free and data-driven methodological framework, its application to MG energy management is still challenging, driven by their limitations on environment status perception and constraint satisfaction. In this paper, the MG energy management problem is formalized as a Constrained Markov Decision Process, and is solved with the state-of-the-art interior-point policy optimization (IPO) method. In contrast to conventional DRL approaches, IPO facilitates efficient learning in multi-dimensional, continuous state and action spaces, while promising satisfaction of complex network constraints of the distribution network. The generalization capability of IPO is further enhanced through the extraction of spatial-temporal correlation features from original MG operating status, combining the strength of edge conditioned convolutional network and long short-term memory network. Case studies based on an IEEE 15-bus and 123-bus test feeders with real-world data demonstrate the superior performance of the proposed method in improving MG cost effectiveness, safeguarding the secure operation of the network and uncertainty adaptability, through performance benchmarking against model-based and DRL-based baseline methods. Finally, case studies also analyze the computational and scalability performance of proposed and baseline methods.

Research on the capacity allocation optimization of micro-grid considering wind-PV-ES

To achieve the goal of “2030 carbon peak and 2060 carbon neutralization” and optimize the form of multi-energy utilization in the industrial park, it is very important to fully exploit distributed clean energy and micro-grid energy system in the park. In view of the economy of micro-grid investment, the capacity proportion of wind, solar and energy storage is the key point in the planning and design stage. A capacity allocation optimization strategy of micro-grid considering wind/solar/thermal/energy storage is proposed. The model takes the minimum total cost of the system as the objective function. As the characteristics of load in the industrial park has a poor adjust ability, the energy storage is added to the integrated energy allocation system as a flexible adjustable power supply. The energy capacity allocation of micro-grid in a cement plant park in China is analysed and calculated in this paper. The results show that after the completion of wind power, photovoltaic and thermal power, the annual power generation of the project will increase by about 105540 MWh, save about 31000 tons of standard coal per year, and reduce about 84000 tons of carbon dioxide emissions. As the cost of renewable energy will be further reduced under the sustainable development of clean energy and the trend of future technology development, establishing a clean energy micro-grid in the industrial park can bring considerable economic benefits. This study can provide a reference for the capacity allocation of the energy system in the industrial park at the planning stage.

Distributionally Robust Energy Management for Islanded Microgrids With Variable Moment Information: An MISOCP Approach

The ever-increasing penetration of renewable energy sources (RESs) has brought tremendous challenges to power system energy management problems. In this paper, we develop a distributionally robust (DR) joint chance-constrained microgrid energy management model while the uncertainties stemming from RESs are embedded. The proposed framework minimizes the expected cost function and ensures that the DR joint chance constraints (CCs) will satisfy for any distribution over a promising ambiguity set, which is designed based on the variable moments. The employed ambiguity set can describe the uncertainties more accurately compared with the commonly-used moment metric (i.e., fixed moments). By deriving equivalent second-order cone programming (SOCP) reformulations of the expected objective function and introducing effective approximation methods such as the optimized Bonferroni approximation to deal with the DR joint CCs, the proposed model is finally reduced to a tractable mixed-integer SOCP problem that can readily be solved. Case studies are conducted on a representative test system to corroborate the effectiveness of the suggested approach.

DC Microgrid energy management with correlated uncertainties

DC Microgrid energy management with correlated uncertainties

Optimal stochastic scheduling in residential micro energy grids considering pumped-storage unit and demand response

Improving energy efficiency, production costs, greenhouse gas emissions, and reliability are some of the most important problems in the energy industry. Energy storage systems and demand-side management (DSM) programs are among the existing solutions for removing such problems. This study presents an improved micro energy grid (MEG) to reduce operating costs as well as greenhouse gas emissions using combined cooling, heating, and power (CCHP) systems, wind turbines (WTs), photovoltaic (PV) units, pumped-storage units, and heating and cooling storage units. In this system, pumped-storage units are employed to store energy. Price- and incentive-based programs are employed to obtain better performance on the demand side. Uncertainties of wind speed, solar power, electricity price, and electrical load are considered to achieve more accurate results. In this paper, the Latin hypercube sampling (LHS) method is applied to generate various scenarios. Then, K-means clustering approach is employed to reduce the number of scenarios. By producing and reducing the proposed scenario, the inherent uncertainty in renewables can be covered, which will result in a more efficient model. Meta-heuristic whale optimization algorithm (WOA) and Pareto model are used to solve single- and multi-objective functions and to optimally design the proposed structure considering contradictory functions, respectively. The features of the proposed developed algorithm are simplicity in execution and absence of derivative operator, increase of local and global search capability and consideration of objective functions with different nature. Moreover, fuzzy decision-making mechanism is proposed to select the optimal solution from the Pareto set of solutions. Finally, the proposed approach in different scenarios on the microgrid with various sources is discussed. The results indicate the proposed method properly obtains the optimal solution.

Renewable energy integration in offshore oil and gas installations

As the oil and gas industry seeks to reduce its carbon footprint and align with global decarbonization goals, integrating renewable energy sources into offshore operations presents a promising pathway. This review examines the feasibility of incorporating renewable energy technologies such as offshore wind, solar, and marine energy into the power supply of oil and gas installations. Offshore wind energy, with its high potential and scalability, emerges as a viable option for powering rigs, as demonstrated by projects like Hywind Tampen in Norway, which uses floating wind turbines to supply energy to oil platforms. Solar energy, while more limited in offshore environments due to space and weather constraints, can be part of hybrid systems combining multiple renewable sources. Marine energy, including tidal and wave power, is still in its developmental stages but holds promise for future applications. The review explores hybrid energy systems that integrate renewable sources with conventional energy, such as gas turbines, to ensure continuous power supply. Microgrid technologies and energy storage solutions are also discussed as essential components for balancing renewable energy’s intermittency and optimizing power distribution. Key challenges, such as the harsh marine environment, high initial investment, and the need for new infrastructure, are analyzed alongside potential solutions like technological innovations in floating renewable systems and automated monitoring technologies. Economic feasibility is considered through a cost-benefit analysis, highlighting the potential long-term savings from reduced fuel use and the benefits of regulatory incentives for renewable energy integration. Ultimately, the integration of renewables not only supports decarbonization but also offers operational efficiency and cost reduction opportunities. This review concludes by examining future trends in renewable energy for offshore installations and the potential for fully electrified, sustainable oil and gas platforms.

Carbon-oriented optimal operation strategy based on Stackelberg game for multiple integrated energy microgrids

Given the context of the energy internet and low-carbon electricity, integrated energy microgrids (IEMs) have become an important means of achieving energy conservation and emission reduction. However, energy interactions among multiple IEMs often create multi-subject interest competition patterns. To address this conflict of interest and achieve low-carbon operation, this paper proposes a low-carbon economy operation strategy for multiple IEMs based on a double-layer Stackelberg game model. First, an IEM model that takes carbon trading into account is established, employing the K-means clustering method to address the seasonal variations of photovoltaic data. Secondly, the collaborative operation model of multiple IEMs is built based on Stackelberg game to realize low-carbon economic operation. In addition, a series of multidimensional evaluation indexes are proposed in terms of the economy, efficiency, and the environment. A comprehensive evaluation method combining subjective and objective weights is established to evaluate the proposed IEM operation strategy. A case study shows that the proposed method increases the income and total earnings of each IEM while reducing overall carbon emissions by 34.6%. Therefore, achieving a win-win situation among multiple IEMs.

Design and implementation of Hybrid Renewable energy (PV/Wind/Diesel/Battery) Microgrids for rural areas.

This study examines the variation in sensitivity of a microgrid system comprised of photovoltaics, wind turbines, diesel engines, and batteries. The primary objective is to increase our knowledge of renewable energy resources (RERs) and their technical and economic factors in the context of the conceptual design of a microgrid system. The investigation employs Typhoon HIL software for simulation and testing, concentrating on hybrid PV/Wind/Diesel/Battery systems and devising a perturb & observe (P&O) maximum power point tracking (MPPT) strategy. Additionally, the study investigates the Optimal Power Controlling MPPT technique and the development and implementation of hybrid renewable energy resources (HRES). The Typhoon HIL system is utilized in the power, automotive, and aerospace industries, among others, to simulate and test control systems in real-time. This study presents a control strategy for a microgrid system that combines renewable energy sources such as solar and wind power with reserve power options such as diesel generators and batteries. The coordinated control technique is implemented by employing a centralized control method, effectively managing the flow of electricity from diverse distributed energy resources (DER) and ensuring the microgrid's stability. The findings indicated that the coordinated control method and dynamic models could be utilized to design and optimize microgrid systems. Future research can concentrate on refining the accuracy of the models and verifying the proposed coordinated control method in microgrid systems that operate in the real world.

Integration of renewable energy resources into the water-energy-food nexus–Modeling a demand side management approach and application to a microgrid farm in Morocco

This paper introduces a framework aimed at analyzing the water-energy-food nexus (WEF) within the context of sustainable farming practices utilizing renewable energy sources, specifically Solar PV, to optimize water management efficiency. The focus lies on investigating the interplay between the water-energy-food nexus and both technical and institutional factors. The study particularly delves into the utilization of distributed energy systems and microgrids for electricity distribution. To achieve the objectives outlined, the framework is applied to a case study involving an off-grid farm in Morocco, aligned with the country's “Green Morocco Plan” of 2008. The study employs the AnyMOD open-source modeling framework in combination with the publicly available decision support tool CropWat (Version 8.0). Through this coupling, a linear optimization model is created to assess various irrigation practices, thus evaluating the energy and water supply variations across different crop growth stages. By employing scenario analysis, the study reveals that the integration of a smart microgrid alongside storage technologies proves beneficial in terms of reducing overall system costs. This integration presents cost-effective solutions and enables the establishment of a sustainable energy supply driven by renewable energy resources. Furthermore, the investigation highlights that constraining irrigation to specific hours of the day results in increased storage requirements and higher associated costs. In conclusion, the study underscores that enhancing the water-energy-food nexus through the integration of a renewable-based microgrid is a complex task. However, it significantly contributes to the development of sustainable farming solutions. This research sheds light on the challenges and opportunities associated with aligning renewable energy, water management, and agricultural practices, ultimately facilitating the pursuit of environmentally conscious and efficient farming methodologies.

Optimal scheduling of CCHP-based resilient energy distribution system considering active microgrids' multi-carrier energy transactions

This paper introduces a two-stage two-level optimization method for optimal day-ahead and real-time scheduling of multicarrier energy distribution systems and microgrids. The model considers the incentive-based and price-based demand response programs to encourage microgrids to transact electrical, heating, and cooling energy carriers with the energy distribution system, which is named hereafter as the energy system. Further, the model formulates the resilient operation of the energy system considering the energy transactions with the electrical, heating, and cooling markets. The main contribution of this paper is the integration of demand response procedures of microgrids in energy transactions with the energy system considering the switching of electrical switches and heating and cooling control valves. The optimization process is another contribution of this paper that is decomposed into two stages that consist of day-ahead and real-time horizons. The first stage is also decomposed into two levels that determine the optimal scheduling of the energy system and microgrids in day-ahead markets. The second stage is comprised of two levels that commit the energy system and microgrids resources. A resiliency index is proposed to assess the resiliency of the energy system in shock conditions. The proposed method was simulated for the 123-bus test system. Different types of microgrids, incentive-based and price-based demand response processes were considered. Simulation results confirmed that the proposed method can reduce the costs of residential, industrial, and commercial microgrids by about 4.47%, 3.88%, and 5.47% concerning only the real-time pricing process. Further, the model can increase the aggregated benefits of the energy system in the day-ahead and real-time markets by about 0.608 Million Monetary Units (MMUs) and 1.10 MMUs, respectively.

Adaptive sliding mode control based on fuzzy logic for hybrid smart microgrid energy management system

ABSTRACT In this paper, an adaptive sliding mode controller (ASMC) based on fuzzy logic is applied to control hybrid smart microgrid power systems under uncertainty. The chattering effects can occur if not adjusted properly due to the sliding mode amplitude control law. The approximation of the ASMC law function is performed by Fuzzy logic, and chattering effects are reduced through a low pass filter. The Lyapunov theory proves the stability of the control system and is also applied to stabilise the system’s control mechanism. The hybrid micro-grid system operation, which consists of distribution generation, loads, PWM IGBT inverter and low pass filter, is simulated using MATLAB 2017b. The reference speed is compared with the hybrid microgrid real speed, which was 1.0 p.u. and gradually controlled through ASMC-fuzzy until the speed desired is achieved for power supply stability. A comparison of results between the simulated ASMC-Fuzzy and PI controller indicated a drastic reduction of THD, which satisfies the required ‘below 5% THD level according to IEEE 1547 standard’ required for operation.

A Techno-Economic Optimal Evaluation and Feasibility Analysis of Hybrid Microgrid Electrification for Smart Cities in Tamil Nadu

The cost-effectiveness of the hybrid design of microgrids for determining if a hybrid MG system is necessary is taken into consideration as the study focuses on improving power access in an optimal and economical manner. Both the cost of electricity (COE) and the loss of power supply probability (LPSP) of the hybrid MG design are optimized. The hybrid microgrid design including the photovoltaic (PV)-wind systems with battery storage and diesel generators are modeled and optimized for technical and economic feasibility using MATLAB. The energy management technique is applied between hybrid energy sources and loads. For this hybrid microgrid energy management system, an optimisation approach called spider monkey optimization (SMO) is employed. Using their distinct fission and fusion techniques, spider monkeys always engage in natural foraging behavior. Here, a variety of common constraints related to PV, wind, energy storage elements, inverters, and diesel generators with a variety of cost functions and dimensions are resolved by the proposed optimization method. The SMO optimization method is used to find a utilitarian optimal hybrid energy system configuration with a minimum COE, minimum LPSP, and leading renewable factor to meet the customer load throughout the entire time horizon.

Data-driven non-parametric chance-constrained model predictive control for microgrids energy management using small data batches

This paper presents a stochastic model predictive control approach combined with a time-series forecasting technique to tackle the problem of microgrid energy management in the face of uncertainty. The data-driven non-parametric chance constraint method is used to formulate chance constraints for stochastic model predictive control, while removing the dependency on probability density assumptions of uncertain variables and retaining the linear structure of the resulting optimization problem. The proposed approach is suitable for implementation on systems with limited computational power or limited memory storage, thanks to its simple linear structure and its ability to provide accurate results within pre-defined confidence levels, even when using small data batches. The proposed forecasting and stochastic model predictive control approaches are applied on a numerical example featuring a small grid-connected microgrid with PV generation, a battery storage system, and a non-controllable load, showing the ability to reduce costs by reducing the confidence level, and to satisfy pre-defined confidence levels.

Techno-Environmental Analysis of a Microgrid Energy System in a University Office Complex

The world is undergoing an irreversible shift towards clean energy. Microgrids are recognized as a key technology that holds significant potential to make a substantial difference in this regard. The paper provides a comprehensive overview of how microgrids work and their impact on climate. The research presented in this paper focuses on reducing carbon dioxide (CO2) in the main campus of Qassim University, Saudi Arabia, through the development and implementation of an engineering model that facilitates the installation of a microgrid system designed to meet the university’s sustainability goals. The study aims to explore possible solutions that can reduce emissions in the administrative building (A7) at Qassim University and meet the university environmental plan. Therefore, a comprehensive study is conducted to investigate the potential reduction in emissions associated with the installation of a microgrid system. This microgrid system operates in a grid-connected mode and comprises three main components: the load, a photovoltaic (PV) system, and batteries. The results of the study indicate that the microgrid reveals a notable transition in the primary sources of electricity. Moreover, the microgrid system proves its capability to meet a substantial portion of the daily energy requirements, highlighting its efficiency and effectiveness in addressing energy needs. The findings of this study highlight the significant potential of the proposed model in curbing carbon emissions, as it demonstrates a reduction from 615.8 to 147.4 Mt of CO2. This reduction aligns with the university’s commitment to sustainability and green initiatives. The computed decrease in carbon footprint emphasizes the possibility of the suggested model to encourage sustainable practices among the university community and mitigate the environmental consequences of energy usage.

Technoeconomic Assessment of Electric Vehicles for Optimal Microgrid Energy System Planning

Technoeconomic Assessment of Electric Vehicles for Optimal Microgrid Energy System Planning

Green hydrogen-based energy storage service via power-to-gas technologies integrated with multi-energy microgrid

Power-to-gas (P2G) is a promising solution to the issue of non-dispatchable renewable power generation. However, the high investment costs and low energy efficiency of P2G systems pose challenges. This study designs a green hydrogen-based Energy Storage as a Service (ESaaS) mode to improve the economic efficiency of P2G systems. In this ESaaS mode, the P2G system acts as an energy trading hub. The ESaaS operator manages the system and enables microgrids to access energy storage services. In return, the ESaaS operator generates revenue through electricity and hydrogen trading. To characterize stakeholders' behaviors, we develop a cost-minimizing model for multi-energy microgrid operation and a revenue-maximizing model for P2G investment and operation. To coordinate stakeholders involved in cooperation, we employ Nash bargaining-based model reformulation to determine investment and operation decisions and benefit distribution. Then, logarithm transformation and trading price discretization are introduced to efficiently solve the non-convex and nonlinear reformulated model. Simulation experiments using Shanghai's electricity market and meteorological data demonstrate that the ESaaS mode achieves 100% recovery of surplus renewable electricity and increases the proportion of renewable energy in microgrids from 59% to 83%. The results suggest that green hydrogen-based ESaaS is a feasible solution to increase the utilization rate of renewable energy and bring considerable economic and environmental benefits to all stakeholders.