Distributed Energy Generation Research Articles

Comprehensive benefit evaluation of Carbon reduction in power transmission and transformation projects by TOPSIS method based on combination weighting

Abstract With the continuous promotion of carbon peak carbon neutrality, the investment and construction of new power systems have accelerated, and the construction of a low-carbon economy has become an inevitable trend of the transformation and development of power transmission and transformation projects. In this paper, the FAHP and entropy methods are used to evaluate the economic, social, and social benefits of carbon reduction in power transmission and transformation projects and finally establish a set of comprehensive benefit evaluation systems for carbon reduction in power transmission and transformation projects. Under this evaluation system, the comprehensive benefit of 220 kV power transmission and transformation project in A province is sorted by entropy weight-TOPSIS method. The comprehensive benefit of power transmission and transformation project B >C >A project is finally obtained. At the same time, in the sub-criteria of economic benefits, the index of “enterprise asset-liability ratio” has the largest comprehensive weight. The “using distributed energy generation” index has the largest comprehensive weight in the sub-criteria layer of carbon reduction benefit. In the subsequent construction of power transmission and transformation projects, enterprises can prioritize improving the above indicators to improve carbon reduction efficiency and economy.

Optimizing Distributed Generation in DC Microgrids: A Comprehensive Study

This thorough examination offers a critical analysis of the intricate relationship between Distributed Generation (DG) and DC microgrids. It provides a thorough analysis of basic ideas, sophisticated control techniques, technological developments, and useful applications in actual situations. In the framework of a paradigm shift towards decentralized energy solutions, this study investigates the efficacy of Direct Current (DC) microgrids in integrating and optimizing diverse distributed generation sources. The study conducts a critical analysis of the challenges and possibilities related to various distributed generation technologies and renewable energy systems. For scientists, engineers, and policymakers engaged in the challenging task of incorporating dispersed energy into current systems, it offers crucial perspectives. This review is an important tool that advances our knowledge of distributed generation in DC microgrids. It contributes significantly to the development of discussions on resilient and sustainable energy solutions. In addition to discussing the challenges associated with DG integration, this study highlights real-world examples that highlight the adaptability and efficiency of DC microgrid designs. Because it includes a thorough examination of power electronics, energy storage, and advanced control systems, this study is an invaluable resource for stakeholders hoping to take advantage of DC microgrids. Through the integration of analytical analysis and pragmatic application, this review provides insightful analysis and field assistance. The entire study contributes significantly to the advancement of distributed generation (DG) integration, which is necessary to establish a sustainable and resilient energy environment. It offers the fundamental knowledge required to accomplish successful integration. This review paper offers an in-depth analysis of DG integration in DC microgrids. This review is to provide a comprehensive overview of the dynamic landscape where distributed energy generation and DC microgrids interact, starting with the foundational ideas and moving on to a close examination of the difficulties, innovations in technology, and useful applications.

Low-Carbon Operation Strategy of Park-Level Integrated Energy System with Firefly Algorithm

The integrated energy system at the park level, renowned for its diverse energy complementarity and environmentally friendly attributes, serves as a crucial platform for incorporating novel energy consumption methods. Nevertheless, distributed energy generation, characterized by randomness, fluctuations, and intermittency, is significantly influenced by the surrounding environment. Within the park, the output of multiple devices frequently diverges significantly from the actual demand, potentially resulting in energy waste phenomena, such as the curtailment of wind and solar power. To tackle the dual challenges of balancing energy supply and demand while reducing carbon emissions in the industrial park, this paper introduces a low-carbon integrated energy system that incorporates distributed renewable and clean energy sources. Mathematical models are formulated for the source–grid–load–storage components of this low-carbon integrated energy system. Furthermore, various operational scenarios for the park-level integrated energy system are analyzed. The ultimate goal is to devise an economically viable, low-carbon, and efficient operational strategy for the integrated energy system, aiming to satisfy the diverse objectives of various stakeholders.

Energy exchange optimization for a building-to-building grid: A non-cooperative game-theory method

The growing demand for green energy solutions has led to an increase in innovative distributed energy generation (DEG) architectures, such as buildings with photovoltaic (PV) generation and energy storage systems (ESS). In these distributed grids, buildings with renewable energy generation capabilities can exchange surplus energy to optimize overall system efficiency, increase their profit, and reduce reliance on the main grid. This paper proposes a game-theory method to optimize energy exchange within a building-to-building (B2B) grid. The proposed method formulates the energy exchange problem as a non-cooperative game, where each building owner aims to maximize their welfare by strategically determining their energy trading decisions, including pricing. The game-theory approach considers energy supply and demand, ESS capacity, and cost considerations, including ESS degradation cost. The proposed approach is demonstrated through simulations, showing significant improvements in cost savings compared to building-to-grid (B2Grid) alternatives. Three cases are compared: (1) B2B grid without considering ESS, (2) B2B grid with ESS without considering the ESS degradation cost (ideal case), and (3) B2B grid with ESS considering the ESS degradation cost. The results show the potential of the proposed game-theory in optimizing energy exchange within the B2B grid with daily and operational hours saving of 2.12−6.37 % and 4.57−165.93 %, respectively.

Research on optimization of improved short-term load composite forecasting model based on AM–CNN–Bi–LSTM

Accurate load prediction is a prerequisite for the design, operation, scheduling, and management of energy systems. In the context of the development of smart grids, the extensive integration of highly volatile distributed energy generation into the power system has brought new challenges to the accuracy, reliability, real-time performance, and intelligence of short-term load forecasting. Therefore, this article proposes a novel short-term power load composite prediction model based on AM–CNN–Bi–LSTM. First, CNN is used to extract relevant feature quantities of power load coupling characteristics. Then, AM is used to evaluate the importance of the feature data, highlighting the features that have a greater impact on the prediction results. Finally, the Bi-LSTM network captures bidirectional temporal information from multiple time steps for prediction. Taking one year of measured data as an example, the error comparison of the prediction results of the composite prediction model overlay shows that compared with other models, the composite prediction model has improved prediction accuracy, feature extraction, generalization ability, and other aspects. The research results improve the accuracy of short-term power load forecasting while providing effective model references for decision-making in power system optimization scheduling, safe operation, and reasonable pricing.

Energy Policies and Interconnection Standards for Energy generation Distributed: Global Status, Development Trends and Perspectives in Peru

Energy policies and interconnection standards play a key role in promoting and regulating distributed energy generation globally. The work should examine the current state of global interconnection policies and standards, emerging trends and their impact on clean energy adoption. Globally, energy policies for distributed generation typically focus on marketing renewable energy sources and improving energy efficiency. Many countries implement tax incentives, subsidies and support programs to stimulate the adoption of distributed generation technologies, such as solar panels and small wind systems. The rules of Interconnections often focus on establishing technical standards to ensure the security, stability and reliability of electrical grids, as well as facilitating the connection and seamless integration of distributed generation systems

A comprehensive investigation of DG integration with DC microgrid

This comprehensive review critically analyses the complex correlation between DC microgrids and the incorporation of Distributed Generation (DG). It offers a full evaluation of fundamental principles, advanced control strategies, technology advancements, and practical implementations in real-world scenarios. This research examines the ability of Direct Current (DC) microgrids to effectively integrate and optimize various DG sources within the context of a paradigm shift towards decentralized energy solutions. The paper critically examines the obstacles and opportunities associated with renewable energy systems and different DG technologies. It provides essential insights for researchers, engineers, and politicians who are involved in the complex process of integrating distributed energy into existing systems. This review serves as a crucial resource that contributes to the understanding of DG in DC microgrids. It plays a significant role in developing conversations surrounding energy solutions that are both resilient and sustainable. This paper not only discusses the difficulties related to the integration of DG, but also emphasizes practical examples that demonstrate the flexibility and effectiveness of DC microgrid designs. This study serves as a crucial resource for stakeholders who aim to use the potential of DC microgrids, since it encompasses a comprehensive analysis of power electronics, energy storage, and sophisticated control systems. By combining critical evaluation with practical implementation, this review offers valuable insights and guidance in the field. The complete investigation plays a significant role in advancing the integration of DG, which is essential for creating a resilient and sustainable energy environment. It provides basic information that is needed for achieving effective integration. This review paper provides a comprehensive examination of the integration of DG within DC microgrids. From laying the groundwork with fundamental principles to scrutinizing challenges, technological advancements, and practical applications, this review aims to offer a holistic understanding of the dynamic landscape where DC microgrids intersect with distributed energy generation.

Fault Detection, Classification and Localization Along the Power Grid Line Using Optimized Machine Learning Algorithms

Distributed energy generation increases the need for smart grid monitoring, protection, and control. Localization, classification, and fault detection are essential for addressing any problems immediately and resuming the smart grid as soon as possible. Simultaneously, the capacity to swiftly identify smart grid issues utilizing sensor data and easily accessible frequency and voltage data from PMU devices is a prerequisite of this task. Therefore, this paper proposes new methods using fuzzy logic and adaptive fuzzy neural networks as well as machine learning and meta-heuristic algorithms. First, line voltage is used by a fuzzy thresholding method to estimate when a transmission line defect would develop in less than 1.2 clock cycles. Next, features taken from frequency signals in the real-time interval are utilized to classify the type of error using machine learning systems (decision tree algorithm and random forest algorithm) optimized with wild horse meta-heuristic algorithm. To locate the precise problem location, we finally use a neural fuzzy inference system that is capable of adapting to new data. We employ a simulated power transmission system in MATLAB to test our proposed solutions. Mean square error (MSE) and confusion matrix are used to assess the efficiency of a classifier or detector. For the decision tree algorithm method, the detector attained an acceptable MSE of 2.34e−4 and accuracy of 98.1%, and for the random forest algorithm method, an acceptable MSE of 3.54e−6 and accuracy of 100%. Furthermore, the placement error was less than 153.6 m in any direction along the line.

A cost-benefit secured DAG based analysis of distributed energy generation in urban settings

This study presents a groundbreaking approach to energy management in urban areas, focusing on smart building frameworks. The proposed method integrates a Directed Acyclic Graph (DAG) architecture to enhance data security, transparency, and integrity within the smart building ecosystem. To optimize the energy management system, advanced algorithms, namely Harmony Search (HS) and Artificial Bee Colony (ABC), are employed. Notably, the HS and ABC algorithms undergo modifications to improve their efficiency and convergence properties. The innovative use of these algorithms aims to achieve global solutions in the complex optimization landscape of energy management. The study explores the synergistic effects of DAG, HS, and ABC, providing a robust and secure solution for urban energy challenges. The proposed framework is designed to address the dynamic nature of smart buildings, allowing for adaptive and efficient energy consumption. The findings contribute to the advancement of sustainable urban development, emphasizing the importance of secure data transactions and optimized energy utilization in the context of smart cities. The modified HS and ABC algorithms play a key role in fine-tuning the energy management process, offering a novel and effective approach to address the evolving energy needs of urban areas.

Energy Storage in Urban Areas: The Role of Energy Storage Facilities, a Review

Positive Energy Districts can be defined as connected urban areas, or energy-efficient and flexible buildings, which emit zero greenhouse gases and manage surpluses of renewable energy production. Energy storage is crucial for providing flexibility and supporting renewable energy integration into the energy system. It can balance centralized and distributed energy generation, while contributing to energy security. Energy storage can respond to supplement demand, provide flexible generation, and complement grid development. Photovoltaics and wind turbines together with solar thermal systems and biomass are widely used to generate electricity and heating, respectively, coupled with energy system storage facilities for electricity (i.e., batteries) or heat storage using latent or sensible heat. Energy storage technologies are crucial in modern grids and able to avoid peak charges by ensuring the reliability and efficiency of energy supply, while supporting a growing transition to nondepletable power sources. This work aims to broaden the scientific and practical understanding of energy storage in urban areas in order to explore the flexibility potential in adopting feasible solutions at district scale where exploiting the space and resource-saving systems. The main objective is to present and critically discuss the available options for energy storage that can be used in urban areas to collect and distribute stored energy. The concerns regarding the installation and use of Energy Storage Systems are analyzed by referring to regulations, and technical and environmental requirements, as part of broader distribution systems, or as separate parts. Electricity, heat energy, and hydrogen are the most favorable types of storage. However, most of them need new regulations, technological improvement, and dissemination of knowledge to all people with the aim of better understanding the benefits provided.

PRINCIPLES OF FORMING THE OPTIMAL STRUCTURE OF THE ELECTRIC POWER SYSTEM WITH THE INTEGRATED USE OF DISTRIBUTED GENERATION SOURCES

Abstract. The article presents the results of developing the optimal configuration of a hybrid electric power system of distributed generation, taking into account the energy potential of alternative energy in Ukraine. The issues of further development of distributed energy based on the introduction of hybrid systems with renewable sources of distributed energy generation are considered. The potential of alternative energy technologies in Ukraine is analyzed. A comparison of the efficiency of implementation of different types of alternative energy sources by the method of hierarchy analysis is carried out in order to identify their level of prospects for use in complex systems of distributed generation. The general optimal structure of the electric power system of distributed generation is developed. A method for optimizing the composition of integrated distributed generation power systems with renewable energy sources based on expert analysis of energy efficiency and the potential for the development of modern alternative energy technologies in Ukraine is proposed. The application of the performed research will increase the reliability of distributed generation systems and their energy efficiency in the hybrid use of renewable energy sources, provided that the system control is fully automated and its structure is optimised.

Optimal Scheduling of Micro-Energy Grid Based on Pareto Frontier Under Uncertainty and Pollutant Emissions

With the continuous improvement of micro-energy grid, in order to reduce carbon emissions and environmental pollution, a multi-objective optimization operation strategy considering the distributed energy generation uncertainty, environmental factors, and self-supply rate is proposed in this paper. First, to simulate the uncertainty of distributed energy, we used the three-parameter Weibull distribution to simulate the wind power. Then, for the load side, an innovative electric and thermal comprehensive demand response model is proposed. Different from the traditional micro energy network that only considers the heat energy balance, this paper also considers the thermal inertia of buildings to more accurately describe the characteristic of the heat load. Meanwhile, on the basis of economic factors, the abandon rate of distributed energy, environmental coefficient and self-sufficiency rate are also regarded as three objective functions. Finally, in order to obtaining a set of solutions at the Pareto front, the entropy-weighted TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method is introduced to obtain the global optimal solution. Simulation study of multiple scenarios shows the effectiveness of the proposed method. Compared with the method that considers only economic costs, the pollutant emissions are reduced by 50kg and self-power supply rate is increased by 20%.

Distributed energy generation based on jet-vortex bioheat generators

The article explores transforming Ukraine’s energy system using the “Smart Grid” concept and distributed generation. It emphasizes the need for decentralizing thermal and electric energy sources with small-scale, renewable local biofuels. The significance of thermochemical distribution systems for bio-waste using jet-vortex generator-incinerators in energy-intensive technologies is highlighted. New methods for generating thermal energy in agro-industrial processes, such as grain drying, are proposed. Heat-technical means for bio-waste decomposition, heat exchange, and combustion product utilization have been developed. Production tests of automated jet-vortex generator-incinerators were conducted, considering variable technological parameters. The cost of thermal energy for grain drying is significantly lower than traditional methods. The process, with a functional heat exchanger, uses preheated clean air, improving grain quality. A cogeneration plant project, based on a generator-incinerator and air turbine, is proposed to address Ukraine’s electricity sector challenges. This plant converts thermal energy into mechanical energy using a heat exchanger and compressor. Electrical energy is produced with an electromechanical converter to meet the thermal-technical module’s needs. The cogeneration plant achieves a total efficiency of approximately 80%.

A multi-objective optimization solution for distributed generation energy management in microgrids with hybrid energy sources and battery storage system

This manuscript proposes an intelligent Golden Jackal Optimization (GJO) for distributed-generation energy management (EM) issues in battery storage systems (BSSs) and hybrid energy sources (HESs). The objectives of the proposed method are to minimize the operating cost, and solve the microgrid (MG) energy management problem. Numerous constraints, including power balance, generation capacity, consumer loads, and the charging-discharging and dynamic performance of energy storage units, have an impact on microgrid energy management system. The proposed approach is run in the MATLAB platform and is compared to existing approaches. Also, the simulation result concludes that the proposed approach has lower costs than the existing methods. The proposed approach provides 96 % high efficiency, and 2×106 $ lower cost compared with other existing Particle Swarm Algorithm (PSO), Artificial Bee Colony (ABC), and Tabu Search (TS) methods.

CO-OPTIMIZATION OF DISTRIBUTED ENERGY RESOURCES UNDER TIME-OF-USE PRICING FRAME

Abstract Distributed energy systems (DES) have been considered as a promising solution due to the benefits on efficiency and environment sides. However, despite the rapid development of distributed energy resources and technologies, the share of the distributed energy generation is still small in comparison to that of traditional generation. Time-of-use (TOU) pricing can be an important incentive strategy to encourage the penetration of distributed energy systems. In this paper, a multi-objective optimization considering the time-of-use pricing impacts is proposed to determine the optimal configuration and capacity of distributed energy system involving different technologies including solar photovoltaic (PV), solar thermal collector (STC), combined heating and power system (CHP), and integrated energy storage (ES). The distributed energy system is designed to satisfy the electric and thermal load of commercial buildings (large hotel and medium office) partially or entirely. The proposed multi-objective optimization is utilized to configurate the optimal combination of distributed energy technologies as well as the system capacity to reduce both the cost and environment impact of the system in different locations. Results show that the proposed optimization method can achieve a trade-off between system cost and environment impact based on the existing time-of-use pricing structure.

Риски энергетического сектора России в условиях низкоуглеродной трансформации мировой экономики

In the context of global climate change, which is evolving into a climate crisis, the importance of the low-carbon transformation of the global economy is increasing. At the same time, many countries are taking steps to intro-duce new measures and regulatory tools aimed at reducing greenhouse gas emissions produced by the ener-gy sector of the world economy. This study aims to identify the risks and uncertainties in Russia’s energy sector amidst low-carbon transformation. It is revealed that the relevant legal framework in our country is fragmented and provides limited incentives for energy enterprises to reduce the carbon intensity of their production. Pivotal transition from traditional to renewable energy sources is possible only with the creation of a comprehensive strategy to reduce greenhouse gas emissions, based on the use of significant administrative and financial re-sources. Addressing the problems of Russia’s energy sector development requires immediate solutions in the field of increasing its efficiency, developing Russian technologies for the use of renewable energy sources, promoting distributed energy generation, as well as implementing the processes of carbon capture and utiliza-tion.

Review of ancillary services and optimal sizing of an energy storage system in a microgrid

The rapid growth of distributed energy generation has brought new challenges for the management and operation of power systems. Voltage fluctuation is one of the primary factors preventing further photovoltaic (PV) penetration in low-voltage networks. The proper sizing and location of any storage system are essential for reducing costs and eliminating losses, which is another element. This review presents an in-depth overview of the different ancillary services that storage systems may offer and a proper sizing of energy storage systems (ESS). Different kinds of ESSs store energy in different ways. Some of these have reached their full potential, while others are still being worked on. Specific storage solutions may be picked depending on how well the application needs to run. Pumped-hydro and thermal energy storage systems are best for large-scale energy storage, while battery energy storage systems are highly suggested for high power and energy needs. Most of the time, supercapacitors, superconducting magnetic energy storage (SMES), and flywheel energy storage (FES) are used for short-term and fast-response uses. Hydrogen and methane are not often used to store energy because they could be more efficient. A few case studies are also given.

Small-scale power generation using stationary gas turbine with renewable energy

This article mainly discusses one of the most important applications of stationary gas turbinesthe small-scale power generation. Micro/Small-scale gas turbines have immense hidden potential due to fuel flexibility, low emissions and maintenance. In recent years, the substantial pressure to address environmental issues has led people to focus more on green technologies, so most of the related applications of micro gas turbines use renewable energy like wind energy or biomass. Small-scale power generation can be applied in commercial and residential areas and areas with worse living conditions, such as rural or remote ones. And it is usually designed to exploit more waste heat for cooling/heating purposes. Anyway, there are still drawbacks exist. These turbines require high angular velocity and advanced electronics that can convert high-frequency electricity. The author surveyed distributed energy generation, biomass power plants and mini/micro gas turbines, some typical examples of small-scale power generation that are also stationary and have combustion processes. This application of stationary gas turbines is relatively more innovative and updated than others, so there are not many reliable researches posted online in recent years. This paper will reference all the researchers who want to work in this field.

Optimal Allocation of Photovoltaic Distributed Generations in Radial Distribution Networks

Photovoltaic distributed generation (PVDG) is a noteworthy form of distributed energy generation that boasts a multitude of advantages. It not only produces absolutely no greenhouse gas emissions but also demands minimal maintenance. Consequently, PVDG has found widespread applications within distribution networks (DNs), particularly in the realm of improving network efficiency. In this research study, the dingo optimization algorithm (DOA) played a pivotal role in optimizing PVDGs with the primary aim of enhancing the performance of DNs. The crux of this optimization effort revolved around formulating an objective function that represented the cumulative active power losses that occurred across all branches of the network. The DOA was then effectively used to evaluate the most suitable capacities and positions for the PVDG units. To address the power flow challenges inherent to DNs, this study used the Newton–Raphson power flow method. To gauge the effectiveness of DOA in allocating PVDG units, it was rigorously compared to other metaheuristic optimization algorithms previously documented in the literature. The entire methodology was implemented using MATLAB and validated using the IEEE 33-bus DN. The performance of the network was scrutinized under normal, light, and heavy loading conditions. Subsequently, the approach was also applied to a practical Ajinde 62-bus DN. The research findings yielded crucial insights. For the IEEE 33-bus DN, it was determined that the optimal locations for PVDG units were buses 13, 25, and 33, with recommended capacities of 833, 532, and 866 kW, respectively. Similarly, in the context of the Ajinde 62-bus network, buses 17, 27, and 33 were identified as the prime locations for PVDGs, each with optimal sizes of 757, 150, and 1097 kW, respectively. Remarkably, the introduction of PVDGs led to substantial enhancements in network performance. For instance, in the IEEE 33-bus DN, the smallest voltage magnitude increased to 0.966 p.u. under normal loads, 0.9971 p.u. under light loads, and 0.96004 p.u. under heavy loads. These improvements translated into a significant reduction in active power losses—61.21% under normal conditions, 17.84% under light loads, and 33.31% under heavy loads. Similarly, in the case of the Ajinde 62-bus DN, the smallest voltage magnitude reached 0.9787 p.u., accompanied by an impressive 71.05% reduction in active power losses. In conclusion, the DOA exhibited remarkable efficacy in the strategic allocation of PVDGs, leading to substantial enhancements in DN performance across diverse loading conditions.

Optimal operation and maintenance of energy storage systems in grid-connected microgrids by deep reinforcement learning

The operation of microgrids, i.e., energy systems composed of distributed energy generation, local loads and energy storage capacity, is challenged by the variability of intermittent energy sources and demands, the stochastic occurrence of unexpected outages of the conventional grid and the degradation of the Energy Storage System (ESS), which is strongly influenced by its operating conditions. To effectively address these challenges, a novel method for combined operation and maintenance management of ESS has been developed. Unlike the currently available solutions, which typically address the one-day-ahead scheduling problem, the present work considers, for the first time, the realistic case of a microgrid in which the ESS degrades and unexpected outages of the conventional grid can occur along the long-time horizons of the entire microgrid lifetimes. The proposed method, which is based on deep reinforcement learning, is tested on a simulated grid-connected microgrid of a residential building equipped with photovoltaic modules and an ESS. The method outperforms other state-of-the-art approaches based on heuristics and metaheuristics by increasing the profit by 15% and reducing the average number of ESS replacements during its lifetime. Therefore, it can be concluded that the proposed DRL-based framework allows achieving prescriptive maintenance since the suggested actions are optimal from the point of view of effectively maximizing the profit and minimizing the maintenance interventions over the entire lifetime of the microgrid.