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The electricity sector plays a key role in achieving zero emissions targets. The required transition will lead to substantial changes in the supply, demand, and distribution of electricity, as well as in stakeholder roles. Future market designs may change substantially to accommodate these changes, address challenges, and take advantage of new opportunities. This paper reviews the characteristics of future carbon-neutral electricity systems and electricity market design options. To provide a guiding framework for the literature review, we transfer the complexity of electricity systems into a three-layer structure: Firstly, we analyze papers that rely on techno-economic modeling of the physical electricity system. As a case study, we analyze various studies focusing on a decarbonized European electricity system in 2050. Secondly, we review papers that investigate the economic behavior and effects of self-interest-seeking stakeholders such as producers, network operators, and consumers. Finally, we review papers focusing on policy and market design questions that guide policymakers in achieving a target physical asset combination while considering the behavior of stakeholders. We highlight common trends and disagreements in the literature, review the main drivers of future markets, and finally provide a mapping between those drivers, challenges, and opportunities. The review concludes that the most promising next step toward a fully comprehensive assessment approach is to combine existing approaches across topical and disciplinary boundaries. [ABSTRACT FROM AUTHOR]

The paper proposes a new stochastic multiobjective technoeconomic model for integrating photovoltaic (PV) and wind energy resources in electricity price (EP)‐driven distribution systems. The primary goal of this paper is to determine the optimal location and capacity for renewable energy‐based distributed generation, specifically PV and wind resources, while considering weather and system uncertainties. These uncertainties include stochastic variations in PV illumination intensity, wind speed, EP, and load fluctuations. To address these uncertainties, the paper employs scenario modeling techniques named as Latin hypercube sampling with Cholesky decomposition. This technique generates multiple correlated scenarios that represent uncertain variables. Subsequently, a scenario reduction technique is applied to identify the scenario with the highest probability. Later, a mathematical model is developed to minimize an objective function that encompasses various factors like system losses, node voltage deviations, the cost of purchasing power from the grid; and simultaneously maximize the total annual energy savings. The objective is to find optimal solutions that strike a balance between different objectives. To obtain an efficient optimum solution, this paper employs an effective meta‐heuristic technique named as JAYA algorithm. The results obtained by the JAYA algorithm are juxtaposed with those obtained using particle swarm optimization and genetic algorithm techniques. The proposed method is evaluated using Institute of Electrical and Electronics Engineers (IEEE) 33‐node and IEEE 69‐node test feeders to validate its feasibility and effectiveness. However, the effectiveness of the proposed method is not limited to any size of test systems. [ABSTRACT FROM AUTHOR]

Since power transformers are the most important pieces of equipment in electricity transmission and distribution systems, special attention must be paid to their maintenance in order to keep them in good condition for a long time. This paper reviews the main steps in the process of diagnosing the health of power transformer insulation, which involves the science of analysing the gases dissolved in power transformer oil for effective identification of faults. An accurate diagnosis of incipient faults is favourable to sustainable development and necessary to maintain a reliable supply of electricity. The methods presented for fault diagnosis in mineral-oil-immersed power transformers are divided into analytical and graphical methods and have been found to be simple, economical and effective. After describing the methods, both their strengths and weaknesses were identified, and over the years, the methods were complemented to provide highly accurate information, validated by field inspections. This paper focuses on practical information and applications to manage maintenance based on accurate and up-to-date data. The contents of this paper will be of particular use to engineers who manufacture, monitor and/or use high-power transformers in the energy sector, as well as to undergraduate, master's and PhD students interested in such applications. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has caused fluctuations in electricity demand, altering people's lifestyles and electricity usage patterns, thereby affecting the accuracy of demand predictions. However, existing studies on electricity forecasting have not adequately considered the incorporation of COVID-19-related features and the analysis of electricity usage characteristics across different regions of the UK. Therefore, this paper, based on data of the UK's national electricity demand, conducts an analysis around the scenario of a large-scale health emergency in society. We explore the changing patterns and regional characteristics of electricity consumption during the COVID-19 pandemic, comparing the forecast results before and during the pandemic to illustrate its impact on the UK's electricity demand. By introducing COVID-19-related features into the models, we compare the forecast results before and after their inclusion. The results indicate that the COVID-19 pandemic has had a certain impact on the electricity prediction in the UK, leading to a 22.8% decrease in prediction accuracy. However, the models' correlation improved with the inclusion of COVID-19-related features, resulting in a 13.2% enhancement in prediction accuracy compared to the previous models. Additionally, the study summarizes other factors influencing electricity demand, such as power imports/exports and clean energy usage, as considerations for electricity distribution planning. This contributes to improving the accuracy of predicting the UK's electricity demand during COVID-19 pandemic, enabling the government to adjust power dispatching plans reasonably based on relevant factors, achieving rational distribution and efficient scheduling of power resources. [ABSTRACT FROM AUTHOR]

Many emerging applications, such as factory automation, electric power distribution, and intelligent transportation systems, require multicast Ultra-Reliable Low-Latency Communications (mURLLC). Since 3GPP Release 17, 5G systems natively support multicast functionality, including multicast Hybrid Automatic Repeat Request and various feedback schemes. Although these features can be promising for mURLLC, the specifications and existing studies fall short in offering guidance on their efficient usage. This paper presents the first comprehensive system-level evaluation of mURLLC, leveraging insights from 3GPP specifications. It points out (i) how mURLLC differs from traditional multicast broadband wireless communications, and (ii) which approaches to provide mURLLC require changing the paradigm compared with the existing solutions. Finally, the paper provides recommendations on how to satisfy strict mURLLC requirements efficiently, i.e., with low channel resource consumption, which increases the capacity of 5G systems for mURLLC. Simulation results show that proper configuration of multicast mechanisms and the corresponding algorithms for mURLLC traffic can reduce resource consumption up to three times compared to the baseline solutions proposed for broadband multicast traffic, which significantly increases the system capacity. [ABSTRACT FROM AUTHOR]

Electricity consumption pattern recognition is the foundation of intelligent electricity distribution data analysis. However, as the scale of electricity consumption data increases, traditional clustering analysis methods encounter bottlenecks such as low computation speed and processing efficiency. To meet the efficient mining needs of massive electricity consumption data, in this paper a parallel processing method of the density-based k-means clustering is presented. First, an initial cluster center selection method based on data sample density is proposed to avoid inaccurate initial cluster center point selection, leading to clustering falling into local optima. The dispersion degree of the data samples within the cluster is also used as an important reference for determining the number of clusters. Subsequently, parallelization of density calculation and clustering for data samples were achieved based on the MapReduce model. Through experiments conducted on Hadoop clusters, it has been shown that the proposed parallel processing method is efficient and feasible, and can provide favorable support for intelligent power allocation decisions. [ABSTRACT FROM AUTHOR]

The expansion of distributed renewable resources, together with increased demand from the electrification of transport and heating sectors, impacts distribution networks significantly. Additionally, the emergence of non-programmable and intermittent generators is set to diminish the dominance of traditional rotating and programmable generation, thereby affecting the overall stability of the system. Nevertheless, the flexibility offered by distributed resources has the potential to alleviate the necessity for network reinforcement and contribute to system stability at competitive costs. Local flexibility procurement should be rooted in local markets, serving as mechanisms to address distribution congestion and coordinate the provision of flexibility for transmission network services. The multitude of existing systems and the interdependence of flexibility services have given rise to diverse solutions, still undergoing experimentation in various countries. This paper aims to scrutinize key projects that have established local flexibility markets, delineating their fundamental characteristics, the most common solutions, identifying prevalent barriers and suggesting potential future improvements. The investigation focuses on the most uncertain aspects of local markets: possible TSO-DSO coordination schemes, the time horizon for the acquisition of services and the baseline definition methodologies. [ABSTRACT FROM AUTHOR]

The ongoing electrification of the transport sector is expected to cause an increase in electricity demand and, therefore, trigger significant network investments to accommodate it. This paper focuses on investment decision-making for electricity distribution grids and specifically on the strategic and incremental investment network planning approaches. In particular, the former involves network planning with the consideration of a long-term multi-stage study horizon, as opposed to a shorter–term view of the future that applies to the latter case. An investment analysis that is carried out underlines the economic savings generated from adopting a strategic investment perspective over an incremental one. These economic savings are achieved from the fact that the associated fixed investment costs are incurred only once in the horizon under strategic planning. On the other hand, incremental planning involves a series of network reinforcement decisions, thereby incurring the fixed cost multiple times. In addition, sensitivity analyses that are carried out capture the effect of key parameters, such as investment cost, discount rate and investment delay, on the generated economic savings. [ABSTRACT FROM AUTHOR]

It is widely recognised that improving the visibility and controllability of distributed energy resources (DERs) within electricity distribution networks will have significant benefits, particularly for the management of low-voltage (LV) and medium-voltage (MV) networks. Much work within the electricity distribution industry is currently focused on improving the visibility of DERs on LV networks. From a control-theoretic perspective, this enables closing the loop between the DER and the control room and enables a shift towards utilising data-driven model-based control strategies for DERs. The result is a system-wide performance that is closer to the theoretical optimal. In the Australian context, several jurisdictions are trialling techniques such as dynamic operating envelopes to enhance DER hosting capacity, using IEEE 2030.5-based architectures, with the implementation of distributed energy resource management (DERMS) systems at the enterprise level still quite limited. While there is significant activity focused on DER behaviour and control techniques by way of inverter grid codes and standards, the core issue of interoperability with distribution management systems (DMSs), market operators or participants, electric vehicles (EVs) or other DERs is still a work in progress. Importantly, this is also an impediment to realising distributed architectures for DER control in the grid. The unique characteristics of Australian distribution networks highlights several challenging problems for DER control and management. The objective of this paper is to provide a broad overview of DER control and management strategies in the Australian context, with an application focus on DER control in distribution network management. [ABSTRACT FROM AUTHOR]

During the energy transition, new types of electrical equipment, especially power electronic devices, are proposed to increase the flexibility of electricity distribution grids. One type is the solid-state transformer (SST), which offers excellent possibilities to improve the voltage quality in electricity distribution grids and integrate hybrid AC/DC grids. This paper compares SST to conventional copper-based power transformers (CPT) with and without an on-load tap changer (OLTC) and with additional downstream converters. For this purpose, a corresponding electricity distribution grid is set up in the power system analysis tool DIgSILENT PowerFactory 2022. A DC generator like a photovoltaic system, a DC load like an electric vehicle fast charging station, and an AC load are connected. Based on load flow simulations, the four power transformers are compared concerning voltage stability during a generator-based and a load-based scenario. The results of load flow simulations show that SSTs are most valuable when additional generators and loads are to be connected to the infrastructure, which would overload the existing grid equipment. The efficiency of using SSTs also depends on the parameters of the electrical grid, especially the lengths of the low-voltage (LV) lines. In addition, a flowchart-based decision process is proposed to support the decision-making process for the appropriate power transformer from an electrical perspective. Beyond these electrical properties, an evaluation matrix lists other relevant criteria like characteristics of the installation site, noise level, expected lifetime, and economic criteria that must be considered. [ABSTRACT FROM AUTHOR]

Assessments of the hosting capacity of electricity distribution networks are of paramount importance, as they facilitate the seamless integration of rooftop photovoltaic systems into the grid, accelerating the transition towards a more carbon neutral and sustainable system. This paper employs a deep reinforcement learning-based approach to evaluate the real-time hosting capacity of low voltage distribution networks in a model-free manner. The proposed approach only requires real-time customer voltage data and solar irradiation data to provide a fast and accurate estimate of real-time hosting capacity at each customer connection point. This study addresses the imperative for accurate electrical models, which are frequently unavailable, in evaluating the hosting capacity of electricity distribution networks. To meet this challenge, the proposed approach utilizes a deep neural network-based, data-driven model of a low-voltage electricity distribution network. This proposed methodology incorporates model-free elements, enhancing its adaptability and robustness. In addition, a comparative analysis between model-based and model-free hosting capacity assessment methods is presented, highlighting their respective strengths and weaknesses. The utilization of the proposed hosting capacity estimation model enables distribution network service providers to make well-informed decisions regarding grid planning, leading to cost minimization. [ABSTRACT FROM AUTHOR]

Energy storage systems (ESS) are seeing rapid market growth due to the changing worldwide landscape of electricity distribution and consumption. An ESS must possess the capability to oversee the functioning of the system's modules under abnormal circumstances, while also having the ability to supervise, manage, and optimize the performance of one or more battery modules. At present, the condition of batteries is assessed based on two factors: the level of charge (SOC) and the overall condition (SOH). By using these two characteristics, it becomes feasible to compute the anticipated battery lifespan and evaluate a battery's efficiency. The assessment of SOH is a crucial determinant in guaranteeing the effectiveness, dependability, and security of batteries in electric vehicles (EVs). Nevertheless, the safety issues resulting from the imprecise estimation and forecasting of battery health status have garnered significant attention in academic circles. This study presents a comprehensive evaluation of several SOH monitoring techniques. In order to achieve this objective, various scientific and technical literatures are examined and the corresponding methodologies are categorized into distinct groupings. The groupings are categorized based on the manner in which the procedure is executed: methods and techniques used in experiments and models. This paper provides a comprehensive overview of the benefits and drawbacks of several SOH assessment and prediction techniques, along with the associated obstacles in SOH estimation. [ABSTRACT FROM AUTHOR]

The aim of this study was to study the security problems faced by an Radio Frequency Identification (RFID) system in the whole process management of intelligent power distribution system equipment. Based on information security risks, the defects are analyzed, and how to protect distributed energy stations from information security threats in the energy Internet environment is discussed. This paper analyzes the new business communication requirements of distribution communication network and the security risks. The results are as follows: in the classification accuracy experiment, the accuracy of all kinds of classification is obtained through three algorithms [DAG-SVM based on minimum coupling degree, DAG-SVM based on maximum separability, and double-decision hyperplane (DDH)]. Among them, the accuracy of the optimized DDH algorithm in this paper is 98.65%, which is the highest of the three, and the time is the shortest, saving about 40%. A security protocol is proposed which has good protection against several attacks. Deep neural network was used to mine the power terminal attack by means of feature engineering and data enhancement, and the network-level security monitoring of power terminals is realized. The feasibility of the proposed method is verified by building a physical simulation platform, and the experimental results show that the proposed strategy can improve the security of power terminals. [ABSTRACT FROM AUTHOR]

The electrical power distribution parts include the distribution transformer responsible for reducing high voltage (HV) 11 Kv to low voltage (LV) 416/240 volts, and the power distribution lines (PDLs), which are the source of electrical power supply for residential neighborhoods. Any malfunction of these parts, result in a power outage. Currently, any faults are reported, detected, and alert in a primitive way, and this mechanism certainly takes a lot of time and effort. In this paper, an automatic approach for detection, report and alert has been suggested. This approach process is carried out by continuously checking the status of the three output feeders (power distribution lines), which is the low voltage side of the distribution transformer, when a malfunction occurs in any one or more of these feeders, it will generate an alert signal in the form of a text message using short message service (SMS). The maintenance center is notified by receiving this SMS via SIM900 modem through the global system for mobile (GSM) communications network. The SMS text message includes feeder number, fault type and location address (street number and distribution transformer number). In this design, time and effort are shortened and the maintenance center enables fully automatic monitoring of electric power distribution parts within the sector of responsibility. [ABSTRACT FROM AUTHOR]

The DC/DC converters are commonly used in DC microgrids to achieve the desired performance of a system under different operating conditions. The dynamics of an isolated DC microgrid with solar photovoltaic panels, battery, and constant power load is a fractional nonlinear model; thus, the complex nonlinear method should be applied for controller design and stabilizing the system. In multi-input multi-output systems with non-linear elements, non-linear control methods should be used, but this paper presents a straightforward technique for designing decentralized controllers for the buck, boost, and buck-boost converters in an isolated DC microgrid to stabilize the system and implement Maximum Power Point Tracking (MPPT) control of the solar photovoltaic panels. The presented methods consider the nonlinear decentralized feedback linearizing control for the nonlinear system. Depending on the performance of the converters in the system, proper dynamic models are introduced for the converters, and decentralized controllers are determined. Various scenarios are simulated and compared to conventional methods. The results indicate that the settling time of the output variables and the overvoltage of the DC-link voltage are reduced by less than 30% compared to other techniques. [ABSTRACT FROM AUTHOR]

The aim of this paper is the optimal scheduling of an active distribution network at the feeder scale to achieve minimum operating costs. To achieve this goal, a two-stage stochastic programming approach is presented that consists of here-and-now and wait-and-see decisions. In order to handle the uncertain parameters in the model, with the Monte Carlo simulation approach, different scenarios were produced, and then the scenarios were reduced. As a result, the mathematical problem is formulated as a linear model programmed in GAMS and then resolved with CPLEX solver. The effectiveness of the proposed model is assured through simulation studies on the standard IEEE 33-bus test system. The operating costs were calculated under different network conditions and were compared among themselves and according to the availability of storage. Obtained operating cost results have indicated that there is 6.4% cost reduction in the case where storage is used under the same conditions, compared to the case where no storage is used. [ABSTRACT FROM AUTHOR]

The paper deals with an optimization problem in an electricity distribution network with different types of distributed generation and a battery storage system in terms of a smart grid concept. The optimization problem considers two objectives, namely, the annual energy losses and the exchange of energy with the higher-level power grid. The decision variables of the problem are the allocation of the different distributed generation units and the battery storage system, the annual power profiles of the controllable distributed generation and the battery storage system, and the power factor profiles of the controllable and noncontrollable distributed generation. All decision variables are solved simultaneously in a single optimization problem. The variable load shapes of the grid consumers and the profiles of the photovoltaic and wind power systems are considered in the study. All data are observed at the annual level with hourly resolution. The problem solving method uses computational intelligence techniques, namely, metaheuristic optimization methods and artificial neural networks. The study proposes a framework for optimizing the decision variables in the planning phase of distributed generation and battery storage, and for controlling the variable power and power factor profiles based on an artificial neural network in the implementation phase. The optimization problem is solved with a power system simulation program and a metaheuristic optimizer in cosimulation synergy. The three cases of distributed generation and battery storage are considered simultaneously. The proposed method is applied to the test grid operator IEEE with 37 buses, and reductions in annual energy losses and energy exchange are obtained in the ranges 34–86% and 41–99%, respectively. [ABSTRACT FROM AUTHOR]

Increasing connection rates of rooftop photovoltaic (PV) systems to electricity distribution networks has become a major concern for the distribution network service providers (DNSPs) due to the inability of existing network infrastructure to accommodate high levels of PV penetration while maintaining voltage regulation and other operational requirements. The solution to this dilemma is to undertake a hosting capacity (HC) study to identify the maximum penetration limit of rooftop PV generation and take necessary actions to enhance the HC of the network. This paper presents a comprehensive review of two topics: HC assessment strategies and reinforcement learning (RL)-based coordinated voltage control schemes. In this paper, the RL-based coordinated voltage control schemes are identified as a means to enhance the HC of electricity distribution networks. RL-based algorithms have been widely used in many power system applications in recent years due to their precise, efficient and model-free decision-making capabilities. A large portion of this paper is dedicated to reviewing RL concepts and recently published literature on RL-based coordinated voltage control schemes. A non-exhaustive classification of RL algorithms for voltage control is presented and key RL parameters for the voltage control problem are identified. Furthermore, critical challenges and risk factors of adopting RL-based methods for coordinated voltage control are discussed. [ABSTRACT FROM AUTHOR]

As the most promising alternative to internal combustion engines (ICEs), electric vehicles (EVs) have an excellent development outlook. The charging route scheduling of EVs can simultaneously affect traffic congestion in the transportation network (TN) and power flow distribution in the power distribution network (PDN). The research on TN and PDN coupling networks based on the static traffic flow model is relatively mature; however, it ignores that the traffic flow will spread across periods in a short scheduling period. In this paper, a semi‐dynamic traffic flow model is proposed to represent the dynamic propagation characteristics of EVs and ICEs flow. Furthermore, the cost of carbon emission and system operation are combined as the overall goal of system optimisation. Since the model has become a more complex non‐linear model, this paper proposes to combine the heuristic sequential boundary tightening and binary expansion method to linearise the model. The study compared four cases and found that a 20% penetration rate of EVs can reduce carbon emissions by 4.2% while reducing the system's total cost by 10%. Moreover, the impact of network congestion on the spatiotemporal distribution of traffic flow and power flow in the coupled network is alleviated. [ABSTRACT FROM AUTHOR]

The quality of electricity is one of the most discussed issues in the regulatory context, structured by guidelines that allow the assessment of efficiency in electricity supply. Regarding electricity distribution systems, it is essential to highlight the aspects associated with the definition of continuity indicators to measure the service provided by the utilities. The parameters used to calculate the indicators are set by specific agencies in each country based on studies and their characteristics. With the growth in demand for electricity, debates associated with reducing greenhouse gas emissions in the atmosphere, and the increase in Distributed Energy Resources (DERs) connected to the grid, a new trend related to the energy supply is verified. Given these new scenarios, this paper presents a literature review in the regulatory field of existing distribution systems in South American countries. The research proposes to analyze, compare, and expose the current models to evaluate their methods and indicators, including a complete case study in Brazil. This paper seeks to illustrate and point out perspectives, challenges, and future perspectives through the collected and analyzed data. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to address an urgent operational issue referring to optimal power flow (OPF), which is associated with a number of technical and financial aspects relating to issues of environmental concern. In the last few decades, OPF has become one of the most significant issues in nonlinear optimization research. OPF generally improves the performance of electric power distribution, transmission, and production within the constraints of the control system. It is the purpose of an OPF to determine the most optimal way to run a power system. For the power system, OPFs can be created with a variety of financial and technical objectives. Based on these findings, this paper proposes the peafowl optimization algorithm (POA). A powerful meta-heuristic optimization algorithm inspired by collective foraging activities among peafowl swarms. By balancing local exploitation with worldwide exploration, the OPF is able to strike a balance between exploration and exploitation. In order to solve optimization problems involving OPF, using the standard IEEE 14-bus and 57-bus electrical network, a POA has been employed to find the optimal values of the control variables. Further, there are five study cases, namely, reducing fuel costs, real energy losses, voltage skew, fuel cost as well as reducing energy loss and voltage skew, and reducing fuel costs as well as reducing energy loss and voltage deviation, as well as reducing emissions costs. The use of these cases facilitates a fair and comprehensive evaluation of the superiority and effectiveness of POA in comparison with the coot optimization algorithm (COOT), golden jackal optimization algorithm (GJO), heap-based optimizer (HPO), leader slime mold algorithm (LSMA), reptile search algorithm (RSA), sand cat optimization algorithm (SCSO), and the skills optimization algorithm (SOA). Based on simulations, POA has been demonstrated to outperform its rivals, including COOT, GJO, HPO, LSMA, RSA, SCSO, and SOA. In addition, the results indicate that POA is capable of identifying the most appropriate worldwide solutions. It is also successfully investigating preferred search locations, ensuring a fast convergence speed and enhancing the search engine's capabilities. [ABSTRACT FROM AUTHOR]

With the development of EVs (Electric Vehicles) and the rapidly developing policies on low carbon and environmental protection, electric power systems and natural gas systems become increasingly larger. Under these circumstances, the V2G (Vehicle-to-grid) and the coordinated operation of an integrated electricity–gas distribution system (IEGDS), considering CO2 emissions, can play a part together in the process of energy conservation. Firstly, the V2G model is discussed; this paper presents the cost differences between out-of-order and order for the car. Secondly, the IEGDS model presents coupling constraints of gas turbines and power-to-gas. Lastly, carbon emission is considered in this paper; a carbon capture plant (CCP) captures the CO2 burning by fossil fuel in the power generation process and stores it in a carbon storage tank. This paper also considers trading with the carbon market via a carbon storage warehouse. With the cooperation of various components, a comprehensive model considers the use of V2G to store power in the IEGDS system, with consideration of the carbon trade. Numerical experiments validate the effectiveness of the combination between V2G and IEGDS, considering carbon emissions and carbon trading. [ABSTRACT FROM AUTHOR]

Electrical power distribution is the most important division in the power supply chain. However, its sustainability in terms of efficiency is very important for the growth of every country. This main objective of the paper is to assess the productivity dynamics of this process using the data envelopment analysis (DEA) methodology to analyse the effectiveness of the electricity distribution regions (EDRs) over a period of 7 years. The paper adapts the biennial Malmquist productivity index by infusing it with the slacks-based measure (SBM) to assess the productivity dynamics of EDRs in Ghana. Productivity dynamics were assessed by decomposing the SBM-BMPI productivity scores into the efficiency, technology, and scale change. It was discovered that the productivity of EDRs in Ghana progressed by 16.23% per annum over the sample period. Productivity was driven mainly by technological change and not the efficiency changes and scale changes. [ABSTRACT FROM AUTHOR]

Renewable energy sources play a crucial role in the provision of power supply in active distribution networks. In networks that rely on renewable energy sources, it may become imperative to curtail the power generated by such units due to a variety of factors. The curtailment of renewable energy generation can be attributed to various factors such as overgeneration, voltage limitations at the network bus, and power limitations at the network line. As per the specified parameters, it is possible for the power generation of renewable units to exceed the load consumption at certain intervals throughout the operational duration. Hence, it is essential to devise a solution that mitigates the frequency and duration of power generation curtailments of renewable units. It is feasible to redistribute overgeneration of renewable energy sources from periods of excess to periods of insufficient capacity to meet demand. This study demonstrates that the provision of power to loads from the sub-distribution substation experiences a reduction in power supply and cost. It is feasible to transmit surplus power from a single bus to multiple other buses. The term used to refer to the ability to transfer power across different locations and time periods is known as spatiotemporal flexibility. The presence of data centers in distribution networks enables a reduction in costs and the frequency of power interruptions associated with renewable energy sources, through the utilization of spatiotemporal flexibility. The objective of this paper is to present a methodology for achieving temporal and spatial flexibility in data centers, with the aim of minimizing costs and curtailing the output generation of renewable energy units. Undoubtedly, this approach can also be executed by incorporating mobile batteries; however, it is noteworthy that mobile batteries entail additional costs on the network. Consequently, the implementation of the spatiotemporal flexibility approach in data centers has been adopted as a substitute for the utilization of mobile batteries. This approach, also referred to as virtual battery, effectively eliminates the costs associated with the use of mobile batteries. This study examines overgeneration of renewable units, bus voltage limitations, and line power restrictions on the standard IEEE 33-bus network. Three scenarios are considered, each involving changes in data center power in response to varying load profiles. The implementation of equitable power distribution across data centers, both in terms of temporal and spatial dimensions, as well as the adoption of flexible spatiotemporal flexibility scenario for data centers, have been observed. The findings of this study indicate that the approach employed in this paper leads to a decrease in costs and the output power of renewable units in the examined scenarios. The implementation of spatiotemporal flexibility mode has resulted in a reduction of power supply expenses ranging from 12 to 37 percent when compared to the basic case. Ultimately, the sensitivity analysis pertaining to the placement of data centers and the associated uncertainty index is presented. • Implementing a model of spatiotemporal flexibility within data centers to function as a virtual storage device. • The curtailment of power generation in renewable energy units has been executed under three different distinct circumstances. • The robust optimization method has been employed to demonstrate the uncertainties inherent in the operation problem. [ABSTRACT FROM AUTHOR]

In recent years, new smart technologies like electric vehicles and active demand management have been introduced to reduce carbon emissions in electricity distribution networks, resulting in altered electricity consumption patterns. However, the impact of the technologies on electricity consumption remains uncertain due to a lack of rigorous evaluation methods and planning techniques accounting for these changes. Addressing these gaps, this paper contributes three key elements to information systems literature. First, this paper presents a policy to plan multi-period electricity distribution networks that are able to take into account changing electricity consumption patterns. This paper uses a policy to determine the impact of new technologies on distribution network investments. Second, this paper determines the effect of network topologies on distribution network investments with altered consumption patterns. Third, this paper develops and solves a novel optimization problem that formalizes the task of long-term distribution network planning under variable consumption patterns. This work holds implications for distribution network management, policy, and research. It offers recommendations for integrating our policies into practical procedures, emphasizing the need for more rigorous planning incentives for policymakers. Furthermore, the large effect of altered consumption patterns shows that actions are needed to design policies that work towards lowering load coincidence. For researchers, the presented problem and solution methods are generalizable and can help researchers in other domains (e.g. parcel delivery, water distribution) to solve comparable planning problems. [ABSTRACT FROM AUTHOR]

• Enhancing the reliability of the electricity distribution system (EDS) in normal conditions by allocating the remote control switches (RCSs) using an MILP formulation. • Improving the resiliency of the EDS. Specifically, aiming to minimize the customers' interruption time and cost by using both the fragility and bathtub curves. • Improving the reliability and resiliency of the EDS by incorporating RCSs and distributed generations (DGs). • Conducting a sensitivity analysis of various factors, such as the customers' load, the investment cost, the RCS operations, and the failure rate. In today's world, power outages significantly impact people's lives. Improving the reliability and resiliency of the electricity distribution system (EDS) can significantly reduce the negative impact of power outages. In this regard, remote control switches (RCSs) are efficient devices that play a crucial role in enhancing the reliability and resiliency of EDSs. However, the challenge lies in strategically placing these switches to minimize power outages. This paper proposes a mixed-integer linear programming (MILP) formulation for the optimal allocation of RCSs. It considers load uncertainties and aims to enhance reliability under typical circumstances while reinforcing resiliency during extreme conditions. The formulation also considers two specific resilience criteria: the duration of interruptions experienced by customers and the total interruption cost incurred by all affected customers in the aftermath of disasters. A system is considered more resilient if individual customers experience fewer power interruptions during disasters. The formulation is rigorously assessed through its implementation on bus four of the Roy Billinton test system (RBTS4), and the outputs demonstrate that the proposed method improves both reliability and resiliency indices. Finally, this paper concludes with technical and economic comparative analyses that examine the effect of changing various parameters. [ABSTRACT FROM AUTHOR]

Electricity infrastructures are critical lifeline systems that are designed to serve with a high degree of reliability the power supply of consumers under normal operating conditions and in case of common failures or expected disturbances. However, many recent weather-induced disasters have brought unprecedented challenges to the electricity networks, underlining that power systems remain unprepared to absorb disruptive large-scale and severe events. Worse still, it is expected that such climate hazards will take place at rising frequency and intensity rates due to climate change. The intensification of meteorological extremes will lead to higher losses and changes in transmission capacity, increasing the frequency and importance of material damage to the aging electric infrastructure, thus resulting in significant disruptions, cascading failures, and unpredictable power outages. This review paper presents real-life examples of different types of extreme weather incidents and their impacts on the distribution network in Greece, a country that is highly vulnerable because of its location, geomorphology, and the existing overhead network assets, highlighting lessons learned related to adaptation options and disaster management best practices. Literature review and benchmarking with other grid operators are also employed to explore resilience-enhancing technical capabilities, weatherproof solutions, and operational strategies on which policy-making initiatives should focus. [ABSTRACT FROM AUTHOR]

Power utilities worldwide are facing enormous challenges when it comes to the distribution of electricity. With these challenges, electricity theft is regarded as the most common challenge in the electrical distribution system. Electricity theft can be meter tampering done in consumer houses and illegal connections done using hook-ups from the distribution pole grids. These electricity theft challenges have caused power utilities to reconsider customer engagements focusing on feedback, putting loss detection systems in their distribution system networks, using artificial intelligence to schedule maintenance and other asset management activities, etc. The main focus of this paper is to design a real-time power theft monitoring and detection system that is able to detect power theft in distribution systems. This proposed system utilizes smart meters consisting of an Arduino ATMega328P microcontrollers with GSM modules (Global System for Mobile Communication) used for system communication. Cloud storage is created to store the smart meter data. Simulations of the proposed system were done using Proteus Design Suite v.8.10 SP3 software. The proposed system is practically constructed for prototype measurement results. Should power imbalances be measured by the system, the authority office will receive an SMS notification as an alert for power theft detected by a specific smart metering system. The authority office will analyse the power measurements sent to the cloud storage (MATLAB Online, ThingSpeak IoT channels display), and further action will be taken. [ABSTRACT FROM AUTHOR]

Energy consumption increases daily across the world. Electricity is the best means that humankind has found for transmitting energy. This can be said regardless of its origin. Energy transmission is crucial for ensuring the efficient and reliable distribution of electricity from power generation sources to end-users. It forms the backbone of modern societies, supporting various sectors such as residential, commercial, and industrial activities. Energy transmission is a fundamental enabler of well-functioning and competitive electricity markets, supporting reliable supply, market integration, price stability, and the integration of renewable energy sources. Electric energy sourced from various regions worldwide is routinely traded within these electricity markets on a daily basis. This paper presents a review of forecasting techniques for intraday electricity markets prices, volumes, and price volatility. Electricity markets operate in a sequential manner, encompassing distinct components such as the day-ahead, intraday, and balancing markets. The intraday market is closely linked to the timely delivery of electricity, as it facilitates the trading and adjustment of electricity supply and demand on the same day of delivery to ensure a balanced and reliable power grid. Accurate forecasts are essential for traders to maximize profits within intraday markets, making forecasting a critical concern in electricity market management. In this review, statistical and econometric approaches, involving various machine learning and ensemble/hybrid techniques, are presented. Overall, the literature highlights the superiority of machine learning and ensemble/hybrid models over statistical models. [ABSTRACT FROM AUTHOR]

Clustering-based reactive voltage partitioning is successful in reducing grid cascading faults, by using clustering methods to categorize different power-consuming entities in the power grid into distinct regions. In reality, each power-consuming entity has different electrical characteristics. Additionally, due to the irregular and uneven distribution of the population, the distribution of electricity consumption is also irregular and uneven. However, the existing method neglects the electrical difference among each entity and the irregular and uneven density distribution of electricity consumption, resulting in poor accuracy and adaptability of these methods. To address these problems, an enhanced density peak model-based power grid reactive voltage partitioning method is proposed in this paper, called EDPVP. First, the power grid is modeled as a weighted reactive network to consider entity electrical differences. Second, the novel local density and density following distance are designed to enhance the density peak model to address the problem that the traditional density peak model cannot adapt to weighted networks. Finally, the enhanced density peak model is further equipped with an optimized cluster centers selection strategy and an updated remaining node assignment strategy, to better identify irregular and uneven density distribution of electricity consumption, and to achieve fast and accurate reactive voltage partition. Experiments on two real power grids demonstrate the effectiveness of the EDPVP. [ABSTRACT FROM AUTHOR]

The use of information technology and the automation of control systems in the energy sector enables a more efficient transmission and distribution of electricity. However, in addition to the many benefits that the deployment of intelligent and largely autonomous systems brings, it also carries risks associated with information and cyber security breaches. Technology systems form a specific and critical communication infrastructure, in which powerful control elements integrating IoT principles and IED devices are present. It also contains intelligent access control systems such as RTU, IDE, HMI, and SCADA systems that provide communication with the data and control center on the outer perimeter. Therefore, the key question is how to comprehensively protect these specialized systems and how to approach security implementation projects in this area. To establish rules, procedures, and techniques to ensure the cyber security of smart grid control systems in the energy sector, it is necessary to understand the security threats and bring appropriate measures to ensure the security of energy distribution. Given the use of a wide range of information and industrial technologies, it is difficult to protect energy distribution systems using standard constraints to protect common IT technologies and business processes. Therefore, as part of a comprehensive approach to cyber security, specifics such as legislative framework, technological constraints, international standards, specialized protocols or company processes, and many others need to be considered. Therefore, the key question is how to comprehensively protect these specialized systems and how to approach security implementation projects in this area. In this article, a basic security concept for control systems of power stations, which are part of the power transmission and distribution system, is presented based on the Smart Grid domain model with emphasis on substation intelligence, according to the Purdue model. The main contribution of the paper is the comprehensive design of mitigation measures divided into mandatory and recommended implementation based on the standards defined within the MITRE ATT&CK matrix specified, concerning the specifications of intelligent distribution substations. The proposed and industry-tested solution is mapped to meet the international security standards ISO 27001 and national legislation reflecting the requirements of NIS2. This ensures that the security requirements will be met when implementing the proposed Security Baseline. [ABSTRACT FROM AUTHOR]

Cooperation in energy systems is no longer limited to the distribution of electricity, and more attention is paid to the trading of green certificates (GCs). This paper proposed a cooperative method for photovoltaic (PV) and electric-tohydrogen (EH) trading, including GC trading under risk management. First, a novel PV and EH model is established and the cooperation mechanism is analyzed. Meanwhile, PV and EH models were risk-controlled using the conditional value at risk to reduce the impact of the uncertainty of PV electricity and EH loads. Then, the PV-EH cooperative model was established based on cooperative game theory; this was then divided into two subproblems of "cooperative benefit maximization" and "transaction payment negotiation," and the above two subproblems were solved distributively by alternating direction multiplier method (ADMM). Only energy transactions and price negotiations were conducted between the PV and EH, which can protect the privacy and confidentiality of each entity. Finally, the effectiveness of the cooperation model was verified using a practical engineering case. The simulation results show that the cooperation of the PV-EH can significantly improve the operational efficiency of each entity and the overall efficiency of the cooperation and realize the efficient redistribution of electricity and GC. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to describe a novel method to compromise between planned (regulated) maintenance and outage initiation and unplanned (unregulated) maintenance and to find an economic model using which one can perform maintenance adequately and in the most optimal state. Design/methodology/approach: In this paper, a system consisting of similar components is considered, and the role of each component in the system is explained. Then, the cost pertaining to failure in each asset is determined. Costs such as energy not supplied, penalties, human resources to resolve the defect and replacing assets are taken into account. Finally, a new comprehensive objective is proposed, and optimization is performed for a sample system. Findings: In this paper, some graphs have been plotted from which plenty of information may be extracted. This is mentioned in the Conclusion. Originality/value: In this paper, some graphs have been plotted from which plenty of information may be extracted. This is mentioned in the Conclusion. [ABSTRACT FROM AUTHOR]

In recent years, the increased distributed generation (DG) capacity in electric distribution systems has been observed. Therefore, it is necessary to research existing structures of distribution networks as well as to develop new (future) system structures. There are many works on the reliability of distribution systems with installed DG sources. This paper deals with a reliability analysis for both present and future medium voltage (MV) electric distribution system structures. The impact of DG technology used and energy source location on the power supply reliability has been analyzed. The reliability models of electrical power devices, conventional and renewable energy sources as well as information and communications technology (ICT) components have been proposed. Main contribution of this paper are the results of performed calculations, which have been analyzed for specific system structures (two typical present network structures and two future network structures), using detailed information on DG types, their locations and power capacities, as well as distribution system automation applied (automatic stand-by switching on—ASS and automatic power restoration—APR). The reliability of the smart grid consisting of the distribution network and the coupled communications network was simulated and assessed. The observations and conclusions based on calculation results have been made. More detailed modeling and consideration of system automation of distribution grids with DG units coupled with the communication systems allows the design and application of more reliable MV network structures. [ABSTRACT FROM AUTHOR]

Background: After notification of the Electricity Act, 2003, the Indian power sector has undergone a major transformation in bringing competition, in the development of electricity industry as a whole, protecting interest of consumers, ensuring supply of electricity to all, reduction of losses and rationalization of electricity tariff, etc. Enough generation capacity has been added, „Power for All? has been implemented in true terms, and both transmission and distribution network has been strengthened considerably. There is still enough work to be done in terms of 100 % metering, and reduction of line losses and commercial losses. Transmission and Distribution (T&D) losses are a percentage of energy lost in the power grid in the process of transporting electricity from generating stations to points of consumption. It is important to measure and understand the source of losses before tackling them. Energy audit of 11 kV lines and Distribution Transformers (DTs) comes into picture here. This paper discusses the challenges in performing the Energy audit in distribution sector and proposes few remedies for the same. Objectives: To understand the difference between the Technical loss, Commercial loss and AT&C loss in power distribution companies; To find out various ways of finding/calculating technical loss, commercial loss and AT&C loss in power distribution companies; To know the significance and method of calculating the Energy Audit of 11 kv feeders and Distribution Transformers; To list out the challenges faced in finding out the Energy Audit of 11 kv feeders and Distribution Transformers; To explore remedies to perform effective energy Audit. Methods: In this paper, various methods of finding out the 11 kv feeder energy audit are discussed. The challenges faced in performing such audit are also listed out. Few remedies to do the audit in a better way is also discussed. [ABSTRACT FROM AUTHOR]

The transition to Electric Vehicles (EV) in place of traditional internal combustion engines is increasing societal demand for electricity. The ability to integrate the additional demand from EV charging into forecasting electricity demand is critical for maintaining the reliability of electricity generation and distribution. Load forecasting studies typically exclude households with home EV charging, focusing on offices, schools, and public charging stations. Moreover, they provide point forecasts which do not offer information about prediction uncertainty. Consequently, this paper proposes the Long Short-Term Memory Bayesian Neural Networks (LSTM-BNNs) for household load forecasting in presence of EV charging. The approach takes advantage of the LSTM model to capture the time dependencies and uses the dropout layer with Bayesian inference to generate prediction intervals. Results show that the proposed LSTM-BNNs achieve accuracy similar to point forecasts with the advantage of prediction intervals. Moreover, the impact of lockdowns related to the COVID-19 pandemic on the load forecasting model is examined, and the analysis shows that there is no major change in the model performance as, for the considered households, the randomness of the EV charging outweighs the change due to pandemic. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had an unprecedented impact on various aspects of our lives, including education, work dynamics, and social interactions. Dealing with the provision of building utilities in such circumstances has become a formidable challenge. During lockdowns, it becomes crucial to allocate resources strategically, giving priority to residential areas over commercial and financial districts based on population density. Identifying residential areas is of utmost importance not only for effective emergency response during natural disasters but also for ensuring fair distribution of electricity and gas when resources are scarce. However, accurately delineating residential zones is challenging due to the intricate nature of urban landscapes. This paper aims to discuss a comprehensive indicator that utilizes open-source intelligence and incorporates a multi-criteria decision-making framework to assess the likelihood of an area being residential. This indicator will greatly assist in optimizing resource allocation for power, gas, and water distribution. To demonstrate the effectiveness of the proposed approach, a case study conducted in Nicosia, Cyprus, is presented. [ABSTRACT FROM AUTHOR]

The UHV DC tension insulator strings are often large‐tonnage suspension porcelain or glass insulators with high mechanical strength. The single insulator is heavy, so two workers working at the same time are required to make a replacement on insulator strings in live working conditions. One of the key problems that must be solved in live working conditions is to select an appropriate way to reach the working position and realize the potential transfer between two workers. In this paper, the electric field calculation model of live working of ±800 kV tension strings was established. The process that two workers enter and leave the working position along the tension string and different conditions that two workers replace insulators were simulated and analyzed, and distribution characteristics of the electric field of insulator strings and live workers were obtained. According to the calculation results, when the two workers replace the tension insulators in different tension strings, it is easy to realize the potential transfer, so as to reach the same potential for work. The study results of this paper provide a basis for the selection of replacement method of large‐tonnage single insulator by two workers, and a theoretical basis for UHV DC live working. [ABSTRACT FROM AUTHOR]

This paper presents a non‐intrusive method for identifying the load state of a distribution network. The method focuses on continuously varying loads. By considering the load on‐off state switching points and the continuous features at on state, a deep convolutional method considering non‐local spatiotemporal features is proposed. The addition of an attention component to the convolutional network enhances the non‐local feature extraction capability of the convolutional network. Ultimately, the effectiveness of the method is demonstrated in an experimental setting. In addition, this paper demonstrates that the proposed method can effectively integrate switching point features as well as persistent features through neural network visualization techniques. [ABSTRACT FROM AUTHOR]

This paper deals with power generation through solar photovoltaic (SPV) system and its implementation as grid‐tied and stand‐alone systems. The major setbacks of SPV system of low conversion efficacy, that can be enhanced through the maximum power point tracking (MPPT) algorithm. A modified incremental conductance (MIC), termed as error based incremental conductance MPPT, has been proposed, and its behaviour is comprehensively compared with classical perturb and observe (P&O) and incremental conductance (IC) techniques. Dealing with the on‐grid application of SPV system, maximum power obtained through MIC is fed to a three‐phase grid operating at unity power factor (UPF), and the quality of grid current is monitored. Further, the power obtained through MIC finds its application in designing an SPV‐diesel generator (DG) based hybrid renewable energy system (HRES) for areas either not connected to the grid or have insufficient fossils. The stochastic nature of source or uncertain load demand leads to deviation in system frequency. This paper proposes an intelligent, comprehensive supervisory optimal‐fuzzy‐proportional‐integral‐derivative (O‐F‐PID) controller for load frequency control (LFC). The performance of proposed O‐F‐PID controller has been vividly compared with the designed optimal‐PID (O‐PID) and conventional PID (C‐PID) controllers under varying source and load demand conditions. [ABSTRACT FROM AUTHOR]

The alternation between cloudy and clear skies alters the photovoltaic production. This makes it necessary to anticipate these disturbances hours in advance for the correct operation of the electricity distribution plants and networks. In this paper, two short-term forecasting models (3 h) are developed to forecast the photovoltaic production in an integrated plant in the CIESOL building of the University of Almería. The methodology used is based on sky camera images and Artificial Intelligence techniques. Two models have been developed and compared applying artificial neural network (ANN) and support vector machine (SVM) techniques. The global irradiance predicted using sky camera images is used as an input variable in both models. In addition, the operational status of the plants has been included as an input parameter through the performance ratio. The results have shown that the errors made by ANN and SVM are very similar. For all sky conditions, the uncertainty of the production forecast differs by less than 2% from the uncertainty of the solar resource, which is the main source of error in the production models developed. [ABSTRACT FROM AUTHOR]

Reliability is an important issue in electricity distribution systems, with strict regulatory policies and investments needed to improve it. This paper presents a mixed integer linear programming (MILP) model for clustering electrical customers, maximizing system reliability and minimizing outage costs. However, the evaluation of reliability and its corresponding nonlinear function represent a significant challenge, making the use of mathematical programming models difficult. The proposed heuristic procedure overcomes this challenge by using a linear formulation of reliability indicators and incorporating them into the MILP model for clustering electrical customers. The model is mainly defined on a density-based heuristic that constrains the set of possible medians, thus dealing with the combinatorial complexity associated with the problem of empowered p-medians. The proposed model proved to be effective in improving the reliability of real electrical distribution systems and reducing compensation costs. Three substation cluster scenarios were explored, in which the total utility compensations were reduced by approximately USD 86,000 (1.80%), USD 67,400 (1.41%), and USD 64,000 (1.3%). The solutions suggest a direct relationship between the reduction in the compensation costs and the system reliability. In addition, the alternative modeling approach to the problem served to match the performance between the distribution system reliability indicators. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new model to estimate efficiency by generalizing the state-of-the-art panel stochastic frontier model, the salient feature of which is decomposition of inefficiency into a persistent and a transient component. The proposed model introduces an autoregressive process to allow for temporal dependence in transient inefficiency. Both firm heterogeneity and persistent inefficiency components are allowed to be correlated with some exogenous and endogenous covariates in the model. Our model solves the endogeneity problem and it also introduces determinants of both persistent and transient inefficiency. Since the transient component is autoregressive, the likelihood function is not available in closed form. To address this problem we use the Maximum Simulated Likelihood and (Simulated or Bayes) Generalized Method of Moments method to estimate the parameters and several other quantities of interest, including transient and persistent inefficiency. Since the model is dynamic and accommodates determinants of inefficiency, it is useful to production managers who wish to identify how much of their present inefficiency is affected by past inefficiency, as well as how and in what ways efficiency can be improved. We use Norwegian electricity distribution data to showcase an application of our model. [ABSTRACT FROM AUTHOR]

The 2022 United Nations Climate Change Conference (COP27) recommended that Egypt be converted to green energy, in addition to increasing the demand for annual energy consumption, which will lead to an increase in the use of renewable energy sources (RES) in Egypt. The Egyptian Ministry of Energy and Electricity plans to build RES (photovoltaic systems and wind farms) connected to the Egyptian power system (EPS). It is a defect to choose the position and size of the RES based on only power calculations because the RES is an intermittent source. This paper presents a modified analytical energy technique for locating RES in IEEE 33-bus and 69-bus distribution networks and a realistic 25-bus 500 kV EPS. An analytical multi-objective function has been developed to determine the optimal locations of DGs or RESs based on power losses and annual energy loss calculations of the system depending on weather conditions. The efficiency and feasibility of the proposed algorithm based on the IEEE 33-bus and 69-bus distribution networks and the realistic 25-bus 500 kV EPS have been tested and compared with PSO and GA. The impact of RESs on the performance of the 25-bus 500 kV EPS has been investigated based on annual energy losses and operation stability depending on weather conditions. The results showed that the proposed technique used these effective values to obtain optimal weather-adjusted locations. The optimal locations of PV systems or wind systems based on energy calculation improved the voltage profile better than power calculation by about 2%, and the annual energy losses decreased by about 7%. The performance of the 25-bus 500 kV EPS, due to the addition of RES, resulted in a decrease in the annual energy losses of 47% and an improvement in the voltage profile and system stability. [ABSTRACT FROM AUTHOR]

Smart metering systems development and implementation in power distribution networks can be seen as an important factor that led to a major technological upgrade and one of the first steps in the transition to smart grids. Besides their main function of power consumption metering, as is demonstrated in this work, the extended implementation of smart metering can be used to support many other important functions in the electricity distribution grid. The present paper proposes a new solution that uses a frequency feature-based method of data time-series provided by the smart metering system to estimate the energy contour at distribution level with the aim of improving the quality of the electricity supply service, of reducing the operational costs and improving the quality of electricity measurement and billing services. The main benefit of this approach is determining future energy demand for optimal energy flow in the utility grid, with the main aims of the best long term energy production and acquisition planning, which lead to lowering energy acquisition costs, optimal capacity planning and real-time adaptation to the unpredicted internal or external electricity distribution branch grid demand changes. Additionally, a contribution to better energy production planning, which is a must for future power networks that benefit from an important renewable energy contribution, is intended. The proposed methodology is validated through a case study based on data supplied by a real power grid from a medium sized populated European region that has both economic usage of electricity—industrial or commercial—and household consumption. The analysis performed in the proposed case study reveals the possibility of accurate energy contour forecasting with an acceptable maximum error. Commonly, an error of 1% was obtained and in the case of the exceptional events considered, a maximum 15% error resulted. [ABSTRACT FROM AUTHOR]

This paper describes a multi-variable robust control scheme for frequency regulation in a diesel–photovoltaic–supercapacitor hybrid power generation system operating in stand-alone mode. The proposed control structure relies on a two-level architecture, with conventional PI-based current tracking controllers placed on the lower control level and receiving references from an H ∞ -control-based upper level. The specific engineering demands of microgrid operation are cast into an H ∞ control formalism. A rapid-prototyping test bench composed of a real supercapacitor-based energy storage system and an emulated diesel–photovoltaic–load grid is developed using real-time digital simulators, namely RT-LAB ® and dSPACE ® , in order to experimentally validate the proposed frequency robust control strategy under realistic operating conditions. [ABSTRACT FROM AUTHOR]