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Currently, all major vehicle manufacturers agree that the future of mobility is electric. All of them began selling electric vehicles (EVs). These types of vehicles require charging, which in turn will have an impact on power distribution systems. All the studies show that the impact on power systems will be significant and may be disastrous if there will be no investments. Unmanaged EV charging can lead to a significant increase in energy costs due to increased level of investment in energy infrastructure. This paper proposes a decentralized EV charging control (DEV-CC) system that can be executed by the existing on-board electronic control units (ECUs) and uses dedicated short-range communication (DSRC) to establish communication between EVs. The proposed DEV-CC adapts the EV charging power depending on the low-voltage distribution network (LVDN) voltage levels measured by the EVs themselves. The main purpose of the proposed DEV-CC is to charge all the EVs connected to the LVDN without allowing the voltage to drop below the imposed limit. As the results show, the proposed DEV-CC manages to charge all EVs while maintaining the voltage levels within the LVDN above the allowable limits. The proposed DEV-CC does not require any investments from the distribution system operator (DSO), can be implemented on EVs with minimal costs and is a viable solution to expensive smart grid systems.

The paper is based on the authors' expertise and scientific research into the coexistence operating of the buildings located in the outskirts of cities near high voltage (HV) power lines. The coexistence study is a technical-economical document in which the overhead lines' mechanical and/or electrical assumptions are defined based on inspections, calculations, measurements, and other methods. Considering the accelerated development of cities nearly the HV power infrastructure, the paper presents the authors expertise based on mechanical calculus about the safety operation of buildings in the coexistence with the overhead power lines. These theories are validated by the results for a real study case.

The use of renewable energy is a priority for both European Union countries and the Republic of Moldova. The main sources of renewable energy that are used today are wind energy and solar energy excluding hydropower. The paper focuses on increasing the efficiency of solar energy use. Usually, solar energy is used in two ways: as electricity by using photovoltaic panels; as thermal energy through the use of solar collectors. Photovoltaic panels have an average efficiency of 6-20%, which means an energy loss of 80% irradiated on its surface. This thermal energy increases the temperature of the photovoltaic cells to 80 °C in summer with strong sun. Heating photovoltaic cells decreases the electrical efficiency by about 0.4% at each degree. The paper proposes an innovative technical solution to increase the efficiency of solar energy use based on photovoltaic panels. The photovoltaic panels are equipped with a cooling system based on tubular capillaries mat, which have a low cost and the integration of a water-to-water heat pump with hot water storage system. The proposed technical solution allows the realization of the trigeneration system: the simultaneous production of electricity, domestic hot water and cold. This system leads to an overall increase in the efficiency of the use of irradiated solar energy on the surface of the photovoltaic panel up to 45% and is much more competitive with the separate use of photovoltaic panels and solar collectors.

The impact of the prosumers on the replacement strategies of the transformers from the electric distribution networks imposed by the Eco-design Directive of the European Commission is studied in the paper. Thus, an original decision-making methodology, integrating two stages, is proposed considering various penetration degrees of the prosumers. In the first stage, the "importance" classes of aged transformer replacement are identified following a clustering process. An optimization model, having as objectives the maximization of the energy-saving and post-payback period, was used to determine the optimal solution associated with the rated power of the transformers from each class. The obtained results demonstrated the efficiency of the proposed methodology for a transformer fleet with 20 units belonging to a Romanian Distribution Network Operator.

The pressure of climate change and the need for the adoption of renewable sources have led to a strong development of technology within microgrids (μG). The modular and scalable nature presents opportunities for the development of a new class of users in μG (i.e. the prosumers). The large-scale integration of small-scale renewable energy sources (SSRES) creates challenges for today's classic electricity distribution networks. The paper proposes a simulation method for optimal integration of prosumers to minimize active power losses in μG. For a deeper analysis regarding the influence of the prosumers on the μG power losses, 24 hour real load consumption and generation profiles are used. The study's findings show that a more effective μG operation by the optimal allocation of the PV panels on the μG can decrease the active power losses up to 35% in a day.

The hydropower sector has gained more importance in the last decade since renewable resources have recorded a worldwide investment boom. Based on its advantages referring to the timely response at peak energy demand with excellent flexibility, hydropower plants must operate as efficiently as possible to cover the energy variations produced by the renewable sources. In the paper, the data mining techniques integrated into a special Knowledge Discovery module of the SCADA system have been proposed to assist the Decision-Maker (DM) in the optimal operation of the hydropower plants. The main advantages refer to the high efficiency of the data processing process and shortening the time interval in which the DM makes the decisions.

In the paper, a clustering-based practical methodology to obtain the typically injected power profiles (TIPPs), which to help the DNOs in understanding the PV prosumers’ behaviour connected to the own networks, was proposed. The installed capacity and annual energy have been considered in the classification process using the K-means clustering algorithm. The TIPPs associated with each season (winter, spring, summer, and autumn) have been determined for the prosumers’ classes obtained in the clustering process. Testing has been done considering all PV prosumers belonging to the households’ category connected in the low voltage distribution networks of a Distribution Network Operator (DNO) from north-eastern Romania. Using the proposed methodology, the DNO can develop the best strategies in the optimal operation of the active distribution networks.

To benefit from all advantages of a hydropower plant, seen as a renewable source, reliable and low cost of the injected electricity in the power grids, the Hydro Plant Operator (HPO) must ensure the optimal operating regimes and an efficient maintenance program. In this context, a clustering- based methodology has been proposed in the paper to determine the representative operating characteristics (ROCs) of a hydropower plant, considering time slices associated with each generating unit. The results have confirmed the performance of the proposed approach from the point of view of the computational time and the quality of partition obtained associated with the patterns of the operating characteristics. These patterns can represent the support offered to the HPO to provide for an optimal operating of a hydropower plant.

The key challenge of smart cities is the need to satisfy the conditions for a safe distance between the buildings, streets, or green areas (public gardens) and the classical high voltage overhead line. If the interference effects are high enough, they may pose a safety hazard to personnel or the public or may compromise their integrity. This paper is the result of the authors expertize that determines how close to high voltage power lines, public gardens can be used. The coexistence study is a technical-economical documentation in which both mechanical and/or electrical assumptions of the overhead lines are delimited based on inspections, calculations, measurements, etc. The location of other capacities or networks on the elements of the corridor or its protection and, where fitting, the obligation expected to satisfy the conditions of coexistence granted for by the rules and the costs of such operations. This research has been limited to generally available materials. Finally, to highlight the impact and risk analysis provided by the coexistence between the high voltage power line and urban public gardens a real study case is presented.

Energy efficiency is a major concern for utilities. For electricity transmission and distribution companies, while efficiency can be achieved by replacing old equipment from the networks with newer devices, or incorporating into the existing infrastructure additional components, these measures should be applied only after all other options, which do not incur additional costs, are exhausted. The paper proposes a metaheuristic approach to solve the phase load balancing problem in low voltage (LV) electricity distribution networks (EDN) that uses a hybrid Differential Evolutionary Particle Swarm Optimization (DEEPSO) algorithm for single-phase consumer allocation on the three phase of the LV network, used for optimal phase load balancing. The results obtained for a real LV distribution feeder from a urban area using the information provided by smart meters demonstrate the performance of proposed approach, with comparison to the classic PSO.

In the paper, a Data Mining methodology is proposed to identify the energy features of the prosumers. The K-means clustering algorithm has been used to obtain prosumers’ categories considering two specific indicators which can help the Distribution Network Operators (DNOs) in the optimal operating of the networks, namely the injected average hourly power and the total annual energy. Testing the methodology has been done for a database corresponding to the prosumers connected in the low voltage (LV) distribution networks belonging to a Romanian DNO in 2019. The obtained results have confirmed the importance of the Data Mining to extract easy and fast the energy features of the prosumers from the large-size databases and used by the DNOs in the Decision-Making process associated with the optimal operating of the distribution networks.

In small distribution systems, the "smart microgrid (µG)" concept is materialized for growing power savings and the allocation of energy distributed sources, and helping distribution system operators (DSOs) to choose the best investment strategies, to achieve a better grid operation, to increase the system efficiency, and to reduce adverse environmental impacts. In this context, the new specialized platforms for an advanced analysis and management of the energy market must be extended to the µG level, by reconsider of the actual grid infrastructures with "smart" µG clusters (µGC). The paper proposes a prosumers fair load sharing and surplus trading approach based on transactive energy concept in µG using an anonymous blockchain trading ledger-based clustering algorithm. In this way the trading process consider a new vision based on µGC for selection the trading peers’ priority solved with Ward hierarchical algorithm. The developed method is tested on a real µG model to check its accuracy. Finally, an analysis regarding traded quantities and pecuniary peers’ benefits is performed.

The assurance of a high quality of the voltage that supplies consumers is a first-degree demand for an optimal operation of the low voltage (LV) micro-grids ($\mu$Gs), being a permanent concern, both for Distribution Network Operators (DNOs) and the final consumers or/end prosumers. The paper proposes a new approach for voltage control in $\mu \mathrm{G}$. The proposed voltage control is solved as an optimization problem. Summarily, the newness of our work is the methodology for optimization the transformer tap changer, solving the twofold objectives as an optimization function for minimize the voltage drops and active power losses. Finally, a test LV unbalanced $\mu$Gs with Small-Scale Renewable Energy Source (SSRES) was analyzed. The results confirm the feasibility of the proposed approach regarding the voltage control through voltage drop improving with 3.25% at last feeders’ pole and the power losses minimization with 4.77 % to the entire $\mu$ G.

In the paper, an efficient method for the optimal allocation of consumers for phase load balancing in low voltage (LV) electrical distribution networks based on the combinational optimization is proposed. The method uses as input data the topology of the distribution network (structure, number of poles, and initial allocation of consumers to each pole and phase) and energy consumption records provided by the smart meters installed at the consumers such that the deviation of currents on the three phases, at the level of each pole to be minimum. The method was tested using a real LV rural, strongly unbalanced network, the obtained results leading to a decreasing of the unbalance factor from 1.287 to 1.003. From the point of view of energy losses, a decrease of these with 65.23% was obtained over the analyzed period (one day).

The paper presents a new approach for determining the impact of the electricity suppliers on greenhouse gas emissions (GHG) using the supplier energy label information. Our particular approach starts when a very important consumer from the paper industry was forced to choose a new supplier because the old one went bankrupt. Because the electricity price can be negotiated, the results emphasize that the consumers must choose suppliers that sell clean energy produced with a preponderance from renewable energy resources with a lowest impact on the environment. The study case was performed at a Romanian level by using information about the first ten most important power suppliers.

The assurance of a high quality of the voltage that supplies consumers is a first-degree demand for an optimal operation of the public electricity distribution networks (EDNs), being a permanent concern, both for Distribution Network Operators (DNOs) and the final consumers. The paper proposes a new optimal voltage control (OVC) approach in EDN using a hybrid line-drop compensation method based on adaptive On-Load Tap Changer (OLTC) Transformers techniques. The OVC problem is split into two separate subproblem which are simultaneously solved. Thus, the paper newness is the automatic OLTC control technique, which must to maintain the optimal operation voltage value in a “controlled” point considering two parameters: the so-called fictitious voltage and the corresponding imaginary impedance. A particular MATLAB code was applied taking into account the voltage quality parameter for a real medium voltage EDN. The results confirm the robustness of the proposed approach both for OVC through OLTC optimization.

This paper presents a model for the optimization of the operation of a distributed trigeneration power plant meant to supply a consumer of electric, thermal and cold energy by two methods - a method of optimization based on genetic algorithms and a method that uses a dedicated software to optimize the power supply to consumers, as well as a comparative analysis of the results obtained by the two methods.

The power losses reduction in electric distribution networks (EDNs) is one of the main tasks for the Distribution System Operators (DSOs). The latest development in the energy storage system (ESS) technologies has introduced new possible applications for EDNs (as microgrids - ${\mu} \mathbf{G}$ ) with radial, tree-like structures. Considering the constant increase in the share of prosumers in the residential sector, the integration and management of ESSs can prove to be a real opportunity to DNOs. The paper proposes a Particle Swarm Optimization (PSO) approach for optimal placement of ESSs in local EDN (or ${\mu} \mathbf{G}$ ). The optimization problem considers the minimization of active energy losses as objective function. Finally, to highlight the feasibility of the proposed approach, a test ${\mu} \mathbf{G}$ was proposed and the simulation results produced by a MATLAB environment implementation are presented. Moreover, the benefits regarding power losses, energy savings and voltage levels are discussed.

The operating conditions in electricity distribution networks become more complex, as the changes in consumption patterns and the extension of retail electricity markets change the traditional demand patterns. As the industrial platforms near cities reduce their activity and the number of residential customers grows, there is an increasing need for voltage compensation and loss management in highly loaded MV and LV distribution networks supplying large residential communities. VAR compensation using capacitor banks remains a cost-effective solution for this problem. This paper presents an improved method for loss reduction in MV distribution networks, using an optimal placement of capacitor banks, with a recently proposed metaheuristic, the Sperm Whale Algorithm (SWA).

Production scheduling represents a significant tool for reducing the cost of electricity bills, flattening the consumption profile, energy conservation, improving the energy efficiency and economic performance, and strengthening competitiveness at small and medium enterprises (SMEs). Considering these aspects, an efficient approach for flattening the electricity consumption profile based on the production scheduling at SMEs was proposed. The optimal solutions were obtained using combinational optimization. The approach was tested at an industrial consumer (a small enterprise in the automobile repair activity domain). The obtained results led at a decrease of peak values of the electricity consumption with 15.87%.

The pressure of climate change and the need to adopt renewable energy sources (RES) have led to an increased development of microgrid technologies. Their modularizable and scalable nature presents opportunities for the development of a new class of players in the power system, i.e. prosumers. The large-scale integration of RES creates acute challenges to the existing power systems. In this context, a reliable and secured balance between energy generation and consumption is highly required. To overcome these problems at microgrid level, a key role is played by the peer-to-peer energy trading using the blockchain concept. In this paper, we present an efficient approach that can be useful for energy trading using blockchain from both active participants (prosumers and consumers). The proposed approach meets the new Romanian regulation regarding prosumers. The results of the study emphasize that the proposed methodology could lead to an efficient balance between local supply and demand with benefits to the participants.

Power savings are becoming fundamental for an efficient management, planning and operation of the electricity distribution networks (EDN) at large enterprise levels. The paper proposes a new approach for additional power losses estimation, which uses measurements taken from a harmonic polluted environment. In order to validate the proposed methodology, the EDN of the a yeast factory from Romania was analyzed. The results demonstrate the ability of the proposed approach to be efficient in the determination of the supplementary power losses, and their influence on energy savings.

The decisions making process for energy efficiency improvement based on technological activities and flows reconfiguration at Small and Medium Enterprises (SMEs) requires a better understanding of the electricity consumption characteristics, and its share on each equipment/installation. In the paper, an expert system-based aggregate method is proposed for electricity consumption forecasting at SMEs. The proposed method was tested at an industrial consumer (a small enterprise in the automobile repair activity domain). The obtained results led at smaller forecasting errors with the average error in the analysed period (a week) by 2.45 %.

In the paper, a hybrid method based on the linear regression model improved with clustering techniques is proposed for the electricity consumption forecasting to Small and Medium Enterprises (SMEs). The method has as its starting point the dependence between electricity consumption and climatic factors (temperature, humidity and wind speed). The testing was made considering the case of a small enterprise and the obtained results led at smaller forecasting errors than in the case of using the simple linear regression method, with the average error in the analysed period (a week) by 2.72 % compared to 4.44 %.

Energy savings represent an important issue both for Distribution Networks Operators (DNOs) and final consumers. For each DNOs, while efficiency can be achieved by replacing old with newer devices, or by updating into the existing configuration another equipment. One of these problems can be solved using the Phase Load Balancing (PLB). This paper proposes a particular approach regarding the aforementioned problem. Then, the PLB problem was solved with the help of two approaches based on the heuristic and PSO algorithms, which use the data provided by Smart Meters. Both algorithms for PLB optimization problem were tested on a real low voltage (LV) electricity distribution network (EDN), and the results, quantified in electricity savings though loss reduction, proving their efficiency.

An analysis on the influence of sampling size used in the profiling process of electricity consumption at Small and Medium Enterprise (SMEs) is presented in this paper. The sampling size is very important because if the production processes inside SMEs have a dynamic change, with more equipment which come in and out of service very fast (seconds or minutes), the profiling of electricity consumption should be made with a small sampling size, such that the shape of representative profiles to reflect the particularities of SMEs about how their electricity is used. For an industrial SME (auto service with complex activities represented by repair, dyeing, tinsmithing, and welding processes), the representative electricity consumption profiles were obtained using the clustering techniques considering a database composed from the recordings of electricity consumption carried out between 1 August and 31 December 2017. The results highlighted that the stages of the production process can be better identified when the characteristic profiles with a sampling size by quarter hour are used, than in the other two considered cases (half hour and hour).

This paper present a new approach for optimal voltage control in power distribution systems considering the minimization of the voltage deviation from reference in all power systems busses using a modified line drop compensation method. Taking into account that the main purpose of the automatic voltage regulator (AVR) for power transformers with on-load-tap-changer (OLTC) is to keep the voltage within a preset limit. By using a fictitious network an algorithm for automatic control of the OLTC transformation ratio has been proposed, which follows practically to maintain the optimum voltage value at a “controlled” point through a fictitious voltage and an imaginary impedance. A MATLAB specialized software was applied to a real distribution network to confirm the feasibility, and numerical results demonstrate the effectiveness and efficiency of the proposed method.

The paper propose a proper approach to the voltage/VAR control problem in distribution systems. Our approach splits the problem in two important tasks and solves them sequentially. The first aspects aim to identify the optimal control of the voltage in all buses, and the second deals with reactive power injections by the capacitors allocation problem. Both of them was solved using a metaheuristic approach, a modified Bat Algorithm and use voltage drop index and power losses minimization as objective functions. The feasibility of the recommended method was tested using a real Romanian distribution grid and the results achieved in MATLAB environment was compared with another two inspired algorithms i.e. the genetic algorithm and fireworks. All this results confirm the efficiency and robustness of our proper modified metaheuristic algorithm with increased efficacy both for diminishing the real losses and voltage profile amelioration in real distribution grids, in accordance also with the energy savings.

Distribution network operators (DNOs) are alarmed for fluctuating behavior of power consumption in the process of decision-making. In the Smart Meters data, outliers can occur from recordings irregularity, undiscovered power demand, illegal user's connection, inadequately installed devices etc. The paper presents an original data mining based approach that acquires the basic concept of knowledge discovery on databases (KDD), focused to identification of outliers recorded by Smart Meters. This information was the input data for power losses estimation in power transformers. In order to validate the proposed approach, a real database with 300 rural substation was considered. By applying the proposed methodology the DNOs know the real technical losses and also a proper operation and planning of actually “smart” networks can be applied.

This paper presents a decision making methodology based on a cost-effectively analysis for the optimal planning of a hybrid energy system (HES), in micro-grids, using HOMER software tool. The proposed methodology consists in analyzing the HES impact that takes into account different types of renewable energy sources (wind, solar and hydro sources) aiming to determine the optimal solution corresponding to a minimum annualized capital cost of HES (ACHES) and a maximum injection degree from the renewable resources.