Distributed Energy Generation Research Articles

An Economical Energy Management Strategy for Viable Microgrid Modes

In the last couple of decades, numerous energy management strategies have been devised to mitigate the effects of greenhouse gas emission, hence introducing the concept of microgrids. In a microgrid, distributed energy generators are used. Microgrid enables a point which ameliorates in exchanging power with the main grid during different times of day. Based on the system constraints, in this work, we aim to efficiently minimize the operating cost of the microgrid and shave the power consumption peaks. For this purpose, we introduce an improved binary bat (iBBat) algorithm which helps to schedule the load demand of smart homes and energy generation from distributed generator of microgrid to the load demand and supply. The proposed energy management algorithm is applied to both grid-connected and islanded modes of the microgrid. The constraints imposed on the algorithm ensure that the load of electricity consumer does not escalate during peak hours. The simulation results are compared with BBat and binary flower pollination algorithm, which validate that the iBBat reflects substantial reduction in operating cost of microgrid. Moreover, results also show a phenomenal reduction in the peak-to-average ratio of load demand from main the main grid.

Microgrids for Disaster Management: An Ethical Risk Perspective

Microgrids are interconnected distributed energy generation and storage systems that can act as either an extension of the existing grid or operate independently of the grid, in so-called island mode [1]. As a power supply technology, microgrids are attractive as they can be more reliable than existing infrastructure and reduce societies? reliance on nonrenewable energy sources [1]. As a valid alternative to reliance on centralized electricity generation and grid distribution, microgrids have value that materializes when disasters render grids inoperable and microgrids remain as islands of service. It is these specific disaster conditions that reveal the latent value of microgrids as disaster management tools, whereas under nondisaster conditions, their value remains hidden. Microgrids can thus be leveraged to manage disaster relief efforts while remaining connected to donor sources and the broader humanitarian relief supply chain.

Comparison of Classical and Smart Transformers Impact on MV Distribution Grid

In last years, the electrical systems are changed from centralized to distributed energy generation. Nowadays, the distributed generation systems consist of many renewable energy sources (RESs) and active loads, connected to the grid by the power electronics converters (PECs). As a result, more often the PECs with novel functionalities like harmonic elimination, voltage sags compensation, grid stability improvement etc. are required. One of the promising solutions for distributed generation system, which allows improve the delivered power is a smart transformer (ST). It is able to replace the classical MV/LV transformer, where additional functionalities are necessary to provide the higher energy quality. Additionally, the ST is characterized by lower weight/volume and provides easy integration of AC and DC sub-grids. Therefore, in this paper, the modelling problem of a modern MV grid with classical and smart transformers have been described. Moreover, the comprehensive simulation analysis of the MV grid was performed, in which different functionalities of ST were considered. The advantages of ST over classical transformer are compared and the impact of ST functionalities on the MV grid is presented, taking into account the currents/voltages distortions in LV side, symmetrization process and voltage support in MV distribution grid as well as the transmission power losses.

Collaborative energy management in a micro-grid by multi-objective mathematical programming

In micro-grids, distributed energy generation based on renewable sources allows reducing the fossil fuel emissions. In order to manage the limited availability of renewable sources and to meet users’ requirements, a proper scheduling of both tasks and storage activities is needed. Moreover, the difficulty of storing energy on a large scale represents an opportunity for the micro-grid actors to collaborate for better balancing energy supply and demand. Collaboration may be achieved through the application of suitable energy management policies tackling the specific consumers’ needs. We address the Energy Management Problem in a micro-grid with collaboration among users. Specifically, a building is considered where each apartment can use both Shared and Local energy resources, the former managed by a Building Manager (BM). The problem is formulated through Bi-objective Mixed Integer Linear Programming, maximizing both the BM and the apartments’ profits and solved through the Augmented ϵ-Constraint approach. Numerical results, obtained on a set of realistic scenarios, are compared to those of the Weighted Sum method and evaluated according to proper performance metrics. The approach flexibility, in terms of selected management policies, is also discussed. Finally, the superiority of the collaborative paradigm versus the non-collaborative one is experimentally proven.

Effect of collection efficiency and oxidation factor on greenhouse gas emission and life cycle cost of landfill distributed energy generation

The landfill gas obtainable from municipal solid waste landfills can be a useful energy carrier for distributed electricity generation if properly harnessed. The quantity of the landfill gas available for use can be greatly influenced by its collection efficiency and the oxidation factor. This paper therefore estimates the possible effect of landfill gas collection efficiency as well as the oxidation factor on the electricity generation potential, life cycle cost and greenhouse gas emission of a landfill distributed generation system. To achieve this objective(s), the municipal solid waste data of the city of Ibadan is used and the amount of landfill gas obtainable in the short and long terms from the decomposition of waste in the landfill is determined using the Landfill Emission Generation Model software. The results showed that the average landfill gas generation rate based on the estimated waste profile of the city was 0.2028 billion cubic metres per year which could produce about 372 Giga Watts hour per year of electricity. It also demonstrated that the collection efficiency and oxidation factor have reciprocating impact on the electricity generation potential obtainable from landfill sites. This study can be found useful for landfill operators, engineers, environmentalists and other stakeholders in waste management sector on the need to operate upgraded landfill sites for improved energy generation and environmental benefits.

A Comparison of Different District Integration for a Distributed Generation System for Heating and Cooling in an Urban Area

The paper proposes a comparison of different district integration options for a distributed generation system for heating and cooling in an urban area. The system considered includes several production units located close to the users, a central unit and the district heating and cooling network which can connect all the users to each other and to a central unit, where a cogeneration system and a solar plant can be placed. Thus, each user can be regarded as isolated from the others, satisfying its energy needs by means of an autonomous production unit. Alternatively, it can be connected to the others through the district heating and cooling network. When a district heating and cooling network is included in the design option the synthesis-design and operation problems cannot be solved separately, because the energy to be produced by each production site is not known in advance, as the flows through the district heating and cooling network are not defined. This paper uses a mixed integer linear programming (MILP) methodology for the multi-objective optimization of the distributed generation energy system, considering the total annual cost for owning, operating and maintaining the whole system as the economic objective function, while the total annual CO2 emissions as the environmental objective function. The energy system is optimized for different district integration option, in order to understand how they affect the optimal solutions compared with both the environmental and economic objects.

Energy Peer-to-Peer Trading in Virtual Microgrids in Smart Grids: A Game-Theoretic Approach

Traditionally, energy consumers pay non-commodity charges (e.g., transmission, environmental and network costs) as a major component of their energy bills. With the distributed energy generation, enabling energy consumption close to producers can minimize such costs. The physically constrained energy prosumers in power networks can be logically grouped into virtual microgrids (VMGs) using telecommunication systems. Prosumer benefits can be optimised by modelling the energy trading interactions among producers and consumers in a VMG as a Stackelberg game in which producers lead and consumers follow. Considering renewable (RES) and non-renewable energy (nRES) resources, and given that RES are unpredictable thus unschedulable, we also describe cost and utility models that include load uncertainty demands of producers. The results show that under Stackelberg equilibrium (SE), the costs incurred by a consumer for procuring either the RES or nRES are significantly reduced while the derived utility by producer is maximized. We further show that when the number of prosumers in the VMG increases, the CO2 emission cost and consequently the energy cost are minimized at the SE. Lastly, we evaluate the peer-to-peer (P2P) energy trading scenario involving noncooperative energy prosumers with and without Stackelberg game. The results show that the P2P energy prosumers attain 47% higher benefits with Stackelberg game.

Development of solar home system with dual energy storage

Distributed energy generation with energy storage is quite important for high penetration of solar PV energy. A solar home system which generates solar power for self-consumption was studied. The solar home system utilizes a switching-type solar PV (HyPV) which operates in either solar or grid mode automatically without feeding solar power into grid. The solar home system also uses dual energy storage consisting of a battery and an electric water heater which stores PV energy as hot water (thermal storage) when the battery is full. Since thermal storage is much cheaper than battery, the dual energy storage would reduce the total cost of solar home system dramatically. Four HyPV solar home systems were built to run long-term field test. The results show that the specific PV energy generation approximates that of feed-in-tariff PV system. A solar PV energy production cost analysis shows that the HyPV solar home system with dual storage is economic if it was used to replace partial energy demand which is paid at higher grid electricity price. This verifies the possibility of grid parity for a solar PV system with energy storage.

Application of Blockchain Technology in Energy Internet Market and Transaction

Based on the blockchain technology, important datas such as transactions, scheduling, finance, and energy contracts are distributedly recorded, various resources such as distributed energy generation, energy transmission, energy consumption, and energy storage are aggregated for collaborative optimization control and market trading. In this paper, we build blockchain-based new generation energy Internet with extensive interconnection, intelligent decision making, real-time interaction, and open sharing. The application of blockchain technology in energy Internet includes photovoltaic energy microgrid, blockchain-based energy Internet entities, blockchain-based power trading, and energy asset securitization. Technical frameworks for energy Internet market trading system based on blockchain technology are proposed. Distributed energy ledger and energy trading smart contract are utilized. Distributed trading rules, algorithms and processes of energy system and microgrid are established. Energy Internet transactions and payment settlement system are constructed. Energy producers and consumers trade peer-to-peer on the platform to improve system efficiency and security.

Biogas production from sewage sludge as a distributed energy generation element: A nationwide case study for Turkey

Sewage sludge is outcome of the wastewater treatment process. It contains hazardous biological and chemical compounds that need to be stabilized. Anaerobic digestion is among the stabilization methods of sewage sludge. Digestion process destroys organic fraction of sewage sludge and produces biogas (%65 Methane, %34 CO2 and etc.). Biogas is burned in internal combustion engines to produce electricity. Digested residue can be used fertilizer. In this study, the total electricity production that can be obtained by anaerobic digestion of all wastewater treatment plants throughout the country is examined. Main objective of this study is preliminary evaluation of energy potential of biogas from sewage sludge anaerobic digestion. Since Wastewater Treatment Plants are distributed in the various regions of a city, above mentioned biogas plants should be considered as distributed generation equipment. Use of small scale energy production plants near the consumers is called distributed generation. Energy transmission losses and related infrastructure cost can be reduced or delayed by means of distributed generation. Within a smart grid approach, mentioned plants can support electricity grid. They can also serve as local emergency power plants. As a nationwide scenario WWTP are evaluated. Biogas energy capacity potential of 234 plants is calculated. Capacities less than 100 kWe are assumed to be non-feasible due to scale economy. It is evident that 91 plants can be installed with an average capacity of 660 kWe.

Local Energy Communities and Distributed Generation: Contrasting Perspectives, and Inevitable Policy Trade-Offs, beyond the Apparent Global Consensus

Although a certain degree of consensus seems to pervade the ideas of “distributed energy systems” and “energy communities”, in truth, diverse views are involved. This article aims to shed light on the variety of interpretations of these two concepts. In particular, the article critically considers the answers to the following four questions: What exactly is meant by “distributed” in the expression “distributed energy generation”? Why is distributed generation (ethically) desirable? Why should people consider it a positive idea that “communities”—and not individuals or families—are invited to manage distributed generation systems? Lastly, can energy communities be considered different from standard state intervention and from market systems? Clearly defining these questions helps in emphasising crucial differences, and it is an important step toward achieving a critical understanding. The conclusion is that there is no single interpretation for either the idea of distributed energy or that of energy communities. Shifting emphasis from one feature to another can drastically affect what policies are required to foster the creation of such communities and of a distributed energy production scenario.

Impacts of Integrating Topology Reconfiguration and Vehicle-to-Grid Technologies on Distribution System Operation

Autonomous electric vehicles (AEVs) provide unique opportunities to cope with the uncertainties of distributed energy generation in distribution networks. But the effects are limited by both inherent radial topology and the behaviors of decentralized AEVs. We investigate the potential benefits of dynamic distribution network reconfiguration (DDNR), taking into account AEVs’ spatial-temporal availability and their charging demand. We propose a mixed integer programming model to optimally coordinate the charging/discharging of AEVs with DDNR, while satisfying AEVs’ original travel plan. Numerical studies based on a test system overlaying the IEEE 33-node test feeder and Sioux Falls transportation network show that DDNR and AEV complement each other, which improves the operation of the distribution system. We also conduct sensitivity analyses on inputs including renewable fluctuation and AEVs penetration level.

Toward Community Generation: Energy Simulation and Performance Evaluation of Multi-family Solar PV Settings for Energy-efficient Homes in Edmonton, Canada

The integration of renewable micro-generation systems into residential buildings, particularly solar photovoltaic (PV) distributed energy generation, is emerging rapidly as an effective method of mitigating the housing impact on greenhouse gas emissions. However, the application of solar PV micro-generation is confronted with several challenges: (a) the average system self-consumption does not exceed 25% in cold-climate regions; (b) most of the energy generated during daytime, peaking in the summer, is exported to the grid; and (c) rebates from the surplus generated energy exported to the grid are at a lesser rate than that of the imported energy. Due to relatively poor economics paralleled with the solar PV application, governments and policy makers envision the value of considering the integration of renewable energy sources at the community level rather than individual behind-the-meter applications, since this strategy can leverage the system self-consumption and increase its social impacts and economics. In this regard, this research aims to simulate and compare the overall performance of two scenarios of a sustainable community of 42 townhouse units. In the first scenario, each unit is connected to a behind-the-meter solar PV system of 3.3-kWp. In the second scenario, all units are connected to a large 140-kWp solar PV system. Historical data from one typical house has been collected (ongoing since 2015). Monte Carlo simulation technique is applied to ensure the stochasticity of the diverse household users. The hourly energy consumption and generation data is simulated using Simphony.NET® simulation engine based on the real-time data collected in Edmonton, Canada. Then, the load-match is identified as well as grid interaction indicators and system economics resulting from both scenarios. Results indicate that the application of community generation can significantly mitigate the imported and exported energy compared with individual behind-the-meter system generation due to the improved system self-consumption.

A Kalman filter-based bottom-up approach for household short-term load forecast

Renewable energy sources are now being used with buildings like PV panels. Consequently, short-term household load forecast plays an important role in managing distributed energy generation, local consumption, and grid-building integration. Forecasting household load, however, can be an intractable problem. These loads are characterized by large uncertainty and variations, leaving much room to improve accuracy. To improve the household load forecast accuracy, this paper advocates a Kalman filter-based bottom-up approach. First, using a deep learning model and a persistence model on public datasets, the authors verified the advantage of the bottom-up approach through granularity analysis at the appliance, room, house levels. Employing the Symmetric Mean Absolute Percentage Error, the authors compared two strategies: (1) the conventional strategy, which forecasts the load directly at the household level, and (2) the bottom-up strategy, which aggregates the forecasts made at the room or appliance level. Experimental results on public datasets demonstrated that the bottom-up approach holds great promise. Second, as the bottom-up approach is often criticized for the cost, the authors designed a recontextualized Kalman filter model to efficiently forecast appliance energy usages. Using two strategies, the authors compared the Kalman filter-based bottom-up approach with deep-learning models. They found the bottom-up approach reduced forecast errors 49% more than the deep-learning models and 47% more than the conventional strategy. Finally, the authors concluded that a Kalman filter-based bottom-up approach could efficiently improve household load forecast accuracy. The findings could help give fast and accurate load forecasts for building energy management and predictive controls.

Two-Stage Energy Management of Multi-Smart Homes With Distributed Generation and Storage

This study presents a new two-stage hybrid optimization algorithm for scheduling the power consumption of households that have distributed energy generation and storage. In the first stage, non-identical home energy management systems (HEMSs) are modeled. HEMS may contain distributed generation systems (DGS) such as PV and wind turbines, distributed storage systems (DSS) such as electric vehicle (EV), and batteries. HEMS organizes the controllable appliances considering user preferences, amount of energy generated/stored and electricity price. A group of optimum consumption schedules for each HEMS is calculated by a Genetic Algorithm (GA). In the second stage, a neighborhood energy management system (NEMS) is established based on Bayesian Game (BG). In this game, HEMSs are players and their pre-determined optimal schedules are their actions. NEMS regulates the total power fluctuations by allowing the energy transfer among households. In the proposed algorithm, HEMS decreases the electricity cost of the users, while NEMS flats the load curve of the neighborhood to prevent overloading of the distribution transformer. The proposed HEMS and NEMS models are implemented from scratch. A survey of 250 participants was conducted to determine user habits. The results of the survey and the proposed system were compared. In conclusion, the proposed hybrid energy management system saves power by up to 25% and decreases cost by 8.7% on average.

Issue: Selected paper of OIPE 2016 on modeling and optimal design of electromagnetic and electronic devices

Issue: Selected paper of OIPE 2016 on modeling and optimal design of electromagnetic and electronic devices

The Design of a Thermoelectric Generator and Its Medical Applications

Growing energy demands are driving people to generate power in every possible way. New energy sources are needed to plug the energy gap. There is a growing interest in distributed energy generation due to its remarkable advantages such as flexibility, reliability, adaptability and minimal transmission losses. Thermoelectric generators (TEGs) are one such distributed power source that relies on thermal energy for electricity generation. The current review focusses on the design and optimization of TEGs to maximize the power output from the available thermal sources. The basic principle of thermoelectricity generation and suitable architecture for specific applications are explained with an overview of materials and manufacturing processes. Various cooling techniques to dissipate heat from the cold side and their influence on overall efficiency are reviewed in this work. Applications of TEGs for powering biomedical sensors have been discussed in detail. Recent advancements in TEGs for various implantable devices and their power requirements are evaluated. The exploitation of TEGs to generate power for wearable sensors has been presented, along with published experimental data. It is envisioned that this study will provide profound knowledge on TEG design for specific applications, which will be helpful for future endeavours.

The Prospects for Wind Energy Development in the Republic of Belarus

The use of wind turbines to create wind energy is one of the main alternatives to the traditional technologies of power generation. The exclusion of combustion products emissions at thermal power plants that operate on hydrocarbon fuel, as well as the exclusion of the fuel component of the cost of electricity generation makes the wind power technology very attractive. However, the rigor of the operation requirements of wind turbines as part of power systems, low density of the flow of primary energy source and the lack of control of it, low utilization of installed capacity, limited operating life, shutdowns in the conditions of squally gusts of wind and ice formation, large areas of alienated land, the impact of noise and infrasonic vibrations and the problems of utilization of large-size structural elements and foundations require a comprehensive analysis of conditions of wind turbines application. Despite the absence of desert areas and of restrictions on the construction of ultra-high structures the analysis of natural and climatic conditions of Belarus demonstrates favorable natural and landscape conditions for the development of wind power generation. The principal task is to choose the location of wind power plants with due regard to environmental requirements, temperature and humidity conditions, terrain and geological features of the location. The results of calculations of the wind flow conditions showed the preference for the joint application of the Weibull and Rayleigh functions that provide the confidence interval of the approximation of the wind speed function, while the terrain specific features make it possible to expect to obtain higher values of the established capacity utilization factor. The development of a distributed energy generation accompanied by Smart Grid technology wide use over electric networks (which would provide new opportunities for consumers and make it possible to eliminate the monopoly of powerful power plants and to reduce burden of basic costs of big power production) ought to be considered as obvious prospect of wind power plants application.

Economic Benefit Evaluation Analysis for Distributed Energy Generation Projects Based on CRITIC-TOPSIS model

Along with the technology reform and equipment promotion, distributed energy generation has taken a great-leap-forward development and becoming more and more flexible and reliable. Nowadays, distributed energy generation has become a critical way to promote the consumption of new energy generation. This paper has established the CRITIC-TOPSIS model to evaluate the economic benefit of distributed energy generation projects. The CRITIC-TOPSIS model was put into example analysis and verified its practicability and rationality.

Recent Developments in Solar Energy-Harvesting Technologies for Building Integration and Distributed Energy Generation

We present a review of the current state of the field for a rapidly evolving group of technologies related to solar energy harvesting in built environments. In particular, we focus on recent achievements in enabling the widespread distributed generation of electric energy assisted by energy capture in semi-transparent or even optically clear glazing systems and building wall areas. Whilst concentrating on recent cutting-edge results achieved in the integration of traditional photovoltaic device types into novel concentrator-type windows and glazings, we compare the main performance characteristics reported with these using more conventional (opaque or semi-transparent) solar cell technologies. A critical overview of the current status and future application potential of multiple existing and emergent energy harvesting technologies for building integration is provided.