Search Results (932)

This paper presents a trajectory control model using finite state machines for a single-stage soft-switching grid-tied inverter designed with a fast dynamic response. The targeted application is a module-integrated inverter for a single photovoltaic (PV) panel which interfaces distributed energy sources with the grid. To minimize switching lossd provide advanced grid-connected functionality, the soft-switching operation is achieved through a resonant filter using a trajectory control scheme. In recent years, controllers based on digital signal processing platforms have been able to handle complex and high-speed control algorithms with precision for real-time control. In real-time control applications, the finite state machine (FSM) approach enhances responsiveness by minimizing latency with limited memory resources by executing rapid state transitions. The proposed model effectively manages the switching states of the single-stage soft-switching inverters during complex DC/AC bidirectional operations. By directly controlling the energy within the series resonant circuit, the model delivers a fast transient response while minimizing switching actions across all quadrants of operation. The control scheme has been digitally implemented on a Texas Instruments (TI) digital signal processor and validated through Hardware-In-the-Loop (HIL) testing. Full article

This paper presents a rule-based control strategy for the Battery Management System (BMS) of a prosumer connected to a low-voltage distribution network. The main objective of this work is to propose a computationally efficient algorithm capable of managing energy flows between the distribution network and a prosumer equipped with a photovoltaic (PV) energy production system. The goal of the BMS is to maximize the prosumer’s economic revenue by optimizing the use, storage, sale, and purchase of PV energy based on electricity market information and daily production/consumption curves. To achieve this goal, the method proposed in this paper consists of developing a rule-based algorithm that manages the prosumer’s Battery Energy Storage System (BESS). The rule-based approach in this type of problem allows for the reduction of computational costs, which is of fundamental importance in contexts where many users will be coordinated simultaneously. This means that the BMS presented in this work could play a vital role in emerging Renewable Energy Communities (RECs). From a general point of view, the method requires an algorithm to process the load and generation profiles of the prosumer for the following three days, together with the hourly price curve. The output is a battery scheduling plan for the timeframe, which is updated every hour. In this paper, the algorithm is validated in terms of economic performance achieved and computational times on two experimental datasets with different scenarios characterized by real productions and loads of prosumers for over a year. The annual economic results are presented in this work, and the proposed rule-based approach is compared with a linear programming optimization algorithm. The comparison highlights similar performance in terms of economic revenue, but the rule-based approach guarantees 30 times lower processing time. Full article

Decentralized planning of renewable energy systems aims to address the substantial spatiotemporal variability, and thus uncertainty, associated with their underlying hydrometeorological processes. For instance, solar photovoltaic (PV) energy is driven by two processes, namely solar radiation, which is the main input, and ambient temperature, with the latter affecting the panel efficiency under specific weather conditions. The objective of this work is to provide a comprehensive investigation of the role of spatial scale by assessing the theoretical advantages of the distributed production of renewable energy sources over those of centralized, in probabilistic means. Acknowledging previous efforts for the optimal spatial distribution of different power units across predetermined locations, often employing the Modern Portfolio Theory framework, this work introduces the generic concept of spatial reliability and highlights its practical use as a strategic planning tool for assessing the benefits of distributed generation at a large scale. The methodology is verified by considering the case of Greece, where PV solar energy is one of the predominant renewables. Following a Monte Carlo approach, thus randomly distributing PVs across well-distributed locations, scaling laws are derived in terms of the spatial probability of capacity factors. Full article

In order to consider the impact of multiple uncertainties on the interaction between the distribution network operator (DNO) and photovoltaic powered charging stations (PVCSs), this paper proposes a regulation strategy for a distribution network with a PVCS based on bi-level stochastic optimization. First, the interaction framework between the DNO and PVCS is established to address the energy management and trading problems of different subjects in the system. Second, considering the uncertainties in the electricity price and PV output, a bi-level stochastic model is constructed with the DNO and PVCS targeting their respective interests. Furthermore, the conditional value-at-risk (CVaR) is introduced to measure the relationship between the DNO’s operational strategy and the uncertain risks. Next, the Karush–Kuhn–Tucker (KKT) conditions and duality theorem are utilized to tackle the challenging bi-level problem, resulting in a mixed-integer second-order cone programming (MISCOP) model. Finally, the effectiveness of the proposed regulation strategy is validated on the modified IEEE 33-bus system. Full article

With the continuous advancement of urbanization, rural areas are facing increasingly severe environmental pollution, excessive energy consumption, and high carbonization resulting from both daily living and production activities. This study, which is aligned with the low-carbon objectives of “carbon sequestration increase and emissions reduction”, explores the optimization strategies for ecological benefits through the combined application of rooftop photovoltaics and rooftop greening in rural residences. Three design approaches are proposed for integrating rooftop photovoltaics with green roofing: singular arrangement, distributed arrangement, and combined arrangement. Using PVsyst (7.4.7) software, this study simulates the effects of roof inclination, system output, and installation formats on the performance of photovoltaic systems, providing a comprehensive analysis of carbon reduction benefits in ecological rooftop construction. A rural area in East China was selected as a sample for adaptive exploration of ecological roof applications. The results of our research indicate that the optimal tilt angle for rooftop photovoltaic (PV) installations in the sample rural area is 17°. Based on simulations combining the region’s annual solar path and the solar parameters on the winter solstice, the minimum spacing for PV arrays is calculated to be 1.925 m. The carbon reduction benefits of the three arrangement methods are ranked, from highest to lowest, as follows: combined arrangement $14530.470{\mathrm{t}\mathrm{C}\mathrm{O}}_{2\mathrm{e}}$ > singular arrangement $11950.761{\mathrm{t}\mathrm{C}\mathrm{O}}_{2\mathrm{e}}$ > distributed arrangement $7444.819{\mathrm{t}\mathrm{C}\mathrm{O}}_{2\mathrm{e}}$. The integrated design of rooftop PV systems and green roofing not only meets the energy demands of buildings but also significantly reduces their carbon footprint, achieving the dual objectives of energy conservation and sustainable development. Therefore, the combined application of rooftop PV systems and green roofing in rural spaces can provide data support and strategic guidance for advancing green transformation and ecological civilization in East China, offering significant practical value for promoting low-carbon rural development. Full article

Energy transition strategies point to energy systems that rely mostly on renewable sources, with photovoltaics being the most commonly used and emphasised. The transition from the past to the future of electrical system is characterised by the contrast between centralised and distributed generation, as well as the differences between synchronous machines and static converters and thus by their way to deliver services required for proper system operation, frequency regulation and transient stability. This paper compares the two converter control strategies, grid following and grid forming, for providing frequency regulation service while considering bulk photovoltaic generation at the HV level and MV-connected distributed by PV generation. The analyses reveal the equivalence between large plants and distributed resources for frequency regulation purposes, highlighting the relevance of grid-forming converter and their ability to supply inertia to the system. These results are obtained for the IEEE 14-bus system implemented in Dig Silent PowerFactory. Full article

A hybrid ship uses integrated generators, an energy storage system (ESS), and photovoltaics (PV) to match its propulsion and service loads, and together with optimal power and voyage scheduling, this can lead to a substantial improvement in ship operation cost, ensuring compliance with the environmental constraints and enhancing ship sustainability. During the operation, significant uncertainties such as waves, wind, and PV result in considerable speed loss, which may lead to voyage delays and operation cost increases. To address this issue, a distributionally robust optimization (DRO) model is proposed to schedule power generation and voyage. The problem is decoupled into a bi-level optimization model, the slave level can be solved directly by commercial solvers, the master level is further formulated as a two-stage DRO model, and linear decision rules and column and constraint generation algorithms are adopted to solve the model. The algorithm aims at minimizing the operation cost, limiting greenhouse gas (GHG) emissions, and satisfying the technical and operational constraints considering the uncertainty. Extensive simulations demonstrate that the expected total cost under the worst-case distribution is minimized, and compared with the conventional robust optimization methods, some distribution information can be incorporated into the ambiguity sets to generate fewer conservative results. This method can fully ensure the on-time arrival of hybrid ships in various uncertain scenarios while achieving expected operation cost minimization and limiting greenhouse gas (GHG) emissions. Full article

The paper proposes a new local method of controlling the on-load tap changer (OLTC) of a transformer to mitigate negative voltage phenomena in low-voltage (LV) networks with a high penetration of photovoltaic (PV) installations. The essence of the method is the use of the load compensation (LC) function with settings determined via artificial neural network (ANN) algorithms. The proposed method was compared with other selected local methods recommended in European regulations, in particular with those currently required by Polish distribution system operators (DSOs). Comparative studies were performed using the model of the 116-bus IEEE test network, taking into account the unbalance in the network and the voltage variation on the medium voltage (MV) side. Full article

The increasing spread of photovoltaic systems for private households (PVs) and electric vehicles (EVs) in order to reduce carbon emissions significantly impacts operation conditions in existing distribution networks. Variable and unpredictable PVs can stress distribution network operation, mainly manifested in voltage violations during the day. On the other hand, variable loads such as EV chargers which have battery storage in their configuration have the ability of storying a surplus energy and, if it is necessary, support a distribution network with energy, commonly known as vehicle-to-grid concept (V2G), to help voltage stability network enhancement. This paper proposes an optimal power flow (OPF)-based model for EV charging to minimize power exchange between the superior-10 kV grid and the observed distribution feeder. The optimization procedure is realized using the co-simulation approach that connects power flow analysis software and optimization method. Three different scenarios are observed and analysed. The first scenario is referred to as a base case without optimization. The second and third scenarios include optimal EV charging and discharging patterns under different constraints. To test the optimization model, a 90-bus unbalanced distribution feeder modelled based on real-life examples is used. The obtained results suggest that this optimization model does not only significantly reduce the power exchange between an external network and the distribution feeder but also improves voltage stability and demand curve in the distribution feeder. Full article

The precision of photovoltaic (PV) output forecasting results is crucial to the reliability of the intelligent distribution network and multi-energy supplementary system. This work aims to address problems of insufficient research related to the short-term prediction of small-sample PV power generation and the low prediction accuracy in the previous research. A hybrid prediction model based on grey relation analysis (GRA) combined with the sparrow search algorithm (SSA) and the grey neural network model (GNNM) is proposed. In this paper, GRA is utilized to reduce the dimension of meteorological features of the samples. Then, the GNNM is used to perform regression analysis on the input features after reducing the dimension of meteorological features of the samples, and the parameters of the GNNM are optimized via SSA. A limited dataset was used to compare several models in different seasons and weather conditions. The prediction results agree well with the data from the PV power plant in Xinjiang, indicating that the GRA-SSA-GNNM model developed in this work effectively achieves a high precision estimation in short-term PV power generation output prediction and has a promising application in this field. Full article

The rapid advancement of battery technology has drawn attention to the effective dispatch of distributed battery storage systems. Batteries offer significant benefits in flexible energy supply and grid support, but maximising their cost-effectiveness remains a challenge. A key issue is balancing conflicts between intentional network services, such as energy arbitrage to reduce the overall electricity costs, and unintentional services, like fault-induced unintentional islanding. This paper presents a novel dispatch methodology that addresses these conflicts by considering both energy arbitrage and unintentional islanding services. First, demand profiles are clustered to reduce uncertainty, and uncertainty sets for photovoltaic (PV) generation and demand are derived. The dispatch strategy is originally formulated as a robust optimal power flow problem, accounting for both economic benefits and risks from unresponsive islanding requests, alongside energy loss reduction to prevent a battery-induced artificial peak. Last, this paper updates the objective function for adapting possible long-run competition changes. The IEEE 33-bus system is utilised to validate the methodology. Case studies show that, by considering the reserve for possible islanding requests, a battery with limited capacity will start to discharge after a demand drop from the peak, leading to the profit dropping from USD 185/day (without reserving capacity) to USD 21/day. It also finds that low-resolution dynamic pricing would be more appropriate for accommodating battery systems. This finding offers valuable guidance for pricing strategies. Full article

This study focuses on the development and implementation of coordinated control and energy management strategies for a photovoltaic–flywheel energy storage system (PV-FESS)-electric vehicle (EV) load microgrid with direct current (DC). A comprehensive PV-FESS microgrid system is constructed, comprising PV power generation, a flywheel energy storage array, and electric vehicle loads. The research delves into the control strategies for each subsystem within the microgrid, investigating both steady-state operations and transitions between different states. A novel energy management strategy, centered on event-driven mode switching, is proposed to ensure the coordinated control and stable operation of the entire system. Based on the simulation results, the PV system cannot cope with the load demand power when it is increased to a maximum of 2800 W, the effectiveness of the individual control strategies, the coordinated control of the subsystems, and the overall energy management approach are confirmed. The main contribution of this research is the development of a coordinated control mechanism that integrates PV generation with FESS and EV loads, ensuring synchronized operation and enhanced stability of the microgrid. This work provides significant insights into optimizing energy distribution and minimizing losses within microgrid systems, thereby advancing the field of energy management in DC microgrids. Full article

The intermittent power generation of renewable energy sources (RESs) interrupts the balance between power generation and demand load due to the increased frequency fluctuation, which challenges the frequency stability analysis and control synthesis of power generation systems. This paper proposes a robust distributed load frequency control (DLFC) scheme for multi-area power systems. Firstly, a multi-area power system is constructed by integrating photovoltaic (PV) and battery energy storage systems (BESSs). Then, by employing the linear matrix inequality (LMI) technique, the sufficient condition capable of ensuring that the proposed controller satisfies $H_{\infty }$ robust performance in the sense of asymptotic stability is derived. Finally, testing is conducted on a four-area renewable power system, and results verify the strong robustness of the proposed controller against load disturbance and intermittence of RESs. Full article

The penetration of solar energy into centralized electric grids has increased significantly during the last decade. Although the electricity from photovoltaics (PVs) can deliver clean and cost-effective energy, the intermittent nature of the sunlight can lead to challenges with electric grid stability. Smart inverter-based resources (IBRs) can be used to mitigate the impact of such high penetration of renewable energy, as well as to support grid reliability by improving the voltage and frequency stability with embedded control functions such as Volt-VAR, Volt–Watt, and Frequency–Watt. In this work, the results of an extensive experimental study of possible interactions between the unstable grid and two residential-scale inverters from different brands under different active and reactive power controls are presented. Two impedance circuits were installed between Power Hardware-in-the-loop (P-HIL) equipment to represent the impedance in an electric distribution line. Grid voltage and frequency were varied between extreme values outside of the normal range to test the response of the two inverters operating under different controls. The key findings highlighted that different inverters that have met the same requirements of IEEE 1547-2018 responded to grid instabilities differently. Therefore, commissioning tests to ensure inverter performance are crucial. In addition to the grid control, the residential PV installed capacity and physical distances between PV homes and the substation, which impacted the distribution wiring impedance which we characterized by the ratio of the reactive to real impedance (X/R), should be considered when assigning the grid-supporting control setpoints to smart inverters. A higher X/R of 3.5 allowed for more effective control to alleviate both voltage and frequency stability. The elimination of deadband in an aggressive Volt-VAR control also enhanced the ability to control voltage during extreme fluctuation. The analysis of sudden spikes in the grid responses to a large frequency drop showed that a shallow slope of 1.5 kW/Hz in the droop control resulted in a >65% lower sudden reactive power overshoot amplitude than a steeper slope of 2.8 kW/Hz. Full article

Ecuador has significant solar potential, and the growing demand calls for sustainable energy solutions. Photovoltaic (PV) microgeneration in buildings is an ideal alternative. Identifying barriers to the widespread adoption of this technology is based on expert consultation and multi-criteria analysis, followed by proposals to overcome these challenges. The methodology of this study includes a systematic literature review (SLR), surveys of industry professionals, and statistical analysis of the collected data. The results highlight barriers such as the high initial cost, government-subsidized tariffs, bureaucratic processes and permits, ineffective regulations, limited awareness, lack of financing, distribution and operational network challenges, and insufficient government incentives. The proposed solutions suggest developing incentive policies to promote investment in PV microgeneration, training programs to enhance technical and cultural knowledge of solar energy, simplifying regulatory processes to facilitate project implementation, and providing accessible financing to reduce economic barriers. Additionally, the recommendations include the implementation of demonstration and outreach projects to showcase the feasibility and benefits of PV microgeneration, thus improving the social and technical acceptance of these systems. These actions aim to foster a faster and more effective energy transition in Ecuador. Full article