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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 the deregulated electricity markets, trading prices are determined by the offer-demand mechanism, and retail consumers can negotiate tariffs with their supplier of choice. For classic wholesale suppliers, the tariffs are determined by the prices of transactions performed on the wholesale market. In parallel with becoming eligible for participating in the market, the consumers use increasingly local generation sources based mostly on renewable electricity generation equipment such as Photovoltaic (PV) panels, and become prosumers. They want to be able to sell back to the market the generation surplus, in order to obtain the maximum benefits from their initial investment. This paper proposes a two-tier local market model oriented for prosumers and consumers connected in microgrids, based on the blockchain technologies and other technologies and concepts such as smart grids, crowdsourcing and energy poverty. Its goals are to improve the possibilities of local prosumers to sell electricity to local consumers and to increase their profitability, compared to the trading model often used in developing markets, of selling the surplus back to the grid via aggregators. The research aims to contribute to the sustainable development of the electricity sector using new and renewable sources of energy, state-of the art technologies and smart contracts, leading to prosumer proliferation and electricity cost reduction for consumers.

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.

In the electric distribution systems, the &ldquo, Smart Grid&rdquo, concept is implemented to encourage energy savings and integration of the innovative technologies, helping the distribution network operators (DNOs) in choosing the investment plans which lead to the optimal operation of the networks and increasing the energy efficiency. In this context, a new phase load balancing algorithm was proposed to be implemented in the low voltage distribution networks with hybrid structures of the consumption points (switchable and non-switchable consumers). It can work in both operation modes (real-time and off-line), uploading information from different databases of the DNO which contain: The consumers&rsquo, characteristics, the real loads of the consumers integrated into the smart metering system (SMS), and the typical load profiles for the consumers non-integrated in the SMS. The algorithm was tested in a real network, having a hybrid structure of the consumption points, on a by 24-h interval. The obtained results were analyzed and compared with other algorithms from the heuristic (minimum count of loads adjustment algorithm) and the metaheuristic (particle swarm optimization and genetic algorithms) categories. The best performances were provided by the proposed algorithm, such that the unbalance coefficient had the smallest value (1.0017). The phase load balancing led to the following technical effects: decrease of the average current in the neutral conductor and the energy losses with 94%, respectively 61.75%, and increase of the minimum value of the phase voltage at the farthest pillar with 7.14%, compared to the unbalanced case.

A growing number of households benefit from government subsidies to install renewable generation facilities such as PV panels, used to gain independence from the grid and provide cheap energy. In the Romanian electricity market, these prosumers can sell their generation surplus only at regulated prices, back to the grid. A way to increase the number of prosumers is to allow them to make higher profit by selling this surplus back into the local network. This would also be an advantage for the consumers, who could pay less for electricity exempt from network tariffs and benefit from lower prices resulting from the competition between prosumers. One way of enabling this type of trade is to use peer-to-peer contracts traded in local markets, run at microgrid (&mu, G) level. This paper presents a new trading platform based on smart peer-to-peer (P2P) contracts for prosumers energy surplus trading in a real local microgrid. Several trading scenarios are proposed, which give the possibility to perform trading based on participants&rsquo, locations, instantaneous active power demand, maximum daily energy demand, and the principle of first come first served implemented in an anonymous blockchain trading ledger. The developed scheme is tested on a low-voltage (LV) microgrid model to check its feasibility of deployment in a real network. A comparative analysis between the proposed scenarios, regarding traded quatities and financial benefits is performed.

Energy losses and bus voltage levels are key parameters in the operation of electricity distribution networks (EDN), in traditional operating conditions or in modern microgrids with renewable and distributed generation sources. Smart grids are set to bring hardware and software tools to improve the operation of electrical networks, using state-of the art demand management at home or system level and advanced network reconfiguration tools. However, for economic reasons, many network operators will still have to resort to low-cost management solutions, such as bus reactive power compensation using optimally placed capacitor banks. This paper approaches the problem of power and energy loss minimization by optimal allocation of capacitor banks (CB) in medium voltage (MV) EDN buses. A comparison is made between five metaheuristic algorithms used for this purpose: the well-established Genetic Algorithm (GA); Particle Swarm Optimization (PSO); and three newer metaheuristics, the Bat Optimization Algorithm (BOA), the Whale Optimization Algorithm (WOA) and the Sperm-Whale Algorithm (SWA). The algorithms are tested on the IEEE 33-bus system and on a real 215-bus EDN from Romania. The newest SWA algorithm gives the best results, for both test systems.

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.

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).

Demand Side Management (DSM) is becoming necessary in residential electricity distribution networks where local electricity trading is implemented. Amongst the DSM tools, Demand Response (DR) is used to engage the consumers in the market by voluntary disconnection of high consumption receptors at peak demand hours. As a part of the transition to Smart Grids, there is a high interest in DR applications for residential consumers connected in intelligent grids which allow remote controlling of receptors by electricity distribution system operators and Home Energy Management Systems (HEMS) at consumer homes. This paper proposes a novel algorithm for multi-objective DR optimization in low voltage distribution networks with unbalanced loads, that takes into account individual consumer comfort settings and several technical objectives for the network operator. Phase load balancing, two approaches for minimum comfort disturbance of consumers and two alternatives for network loss reduction are proposed as objectives for DR. An original and faster method of replacing load flow calculations in the evaluation of the feasible solutions is proposed. A case study demonstrates the capabilities of the algorithm.

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 clustering based data mining method for determining the typical wind power profiles and also to estimate the share of wind power from the total power required by the electrical power system in one year. The proposed method was tested using a real data set with information's about power produced in one year (2016), in Romania. The results demonstrate the efficiency of the methodology to be successfully used in patterns discovery of the wind power profiles.

Increasing the energy efficiency in electric distribution networks is equivalent with making available for the distribution companies of an additional quantity of electricity resulted by reducing the energy losses. Even if the European Union took into account a series of energy efficiency measures policy in the past 10 years for loss reduction and CO2 amounts, an integrated framework to accelerate the use of transformers energy efficient still does not exist. The aim of the paper is to analyse different strategies for replacement of aging transformers which can be profitable for the Distribution Networks Operators (DNOs) from viewpoint of saving energy and can contribute to fulfil the European energy policy targets. The strategies refer to identifying the over loaded and under loaded transformers and replacing them with properly sized transformers having higher energy performance standards. The analyses carried out on a sample of 51 transformers showed an annual cost reduction of energy losses up to 63%.

The paper proposes a heuristic approach to resolve the phase balancing problem in LV distribution networks. The approach is based upon the recordings of electricity of the consumers (endowed with smart meters) for a week-long study period. The results obtained for a real LV distribution feeder that serves a rural area using the information provided by smart meters demonstrate the performance of proposed approach, leading at a decrease of energy losses (from 6.43% to 2.49%).

The paper presents a statistical approach used for detection of outliers from load curves recorded on the electric substation of distribution networks. The load curves provided by smart meters were processed and their main indicators were calculated. By outliers elimination, the remaining data have led to the discovery of accurate patterns that characterized very well the load curves characteristics through indicators. The proposed approach was tested using a real database with 60 substations from a rural area. With the help of these patterns, the operation and planning of electric distribution systems could be made more efficient.

The power distribution networks supply nonlinear consumers which causes a distorted and unbalanced state. A repercussion of these states is represented by the increase of active power losses. Even if the symmetrical sinusoidal state is initially considered for the power losses, in reality this often leads to power lines overloading, especially for the neutral. The existing method for power losses computation in distribution networks with distorted operating state is very difficult to use. The paper investigates the additional power losses for a real distribution network, considering three representative scenarios, close to real operation states. The aim is to present an accurate approach for evaluating the effects of harmonic current flows on the feeder power losses. In order to validate the proposed methodology, a study case is provided. The additional power losses due to the distorted state, for all conductor cross section of the Romanian low voltage power networks are computed.

The basic requirements for a good distribution system are good voltage profile, availability of power on demand and reliability. Capacitors are commonly used to provide the reactive power compensation in distribution systems. This paper investigates a particular approach that highlights the influences which the connection of reactive power sources may have over the solutions for voltage control in a real medium voltage distribution network. The problem of reactive power sources optimization for bus voltage deviation index minimization was solved by optimally placing a number of capacitor banks in the network buses, using the Bat Algorithm. A Matlab specialized software of the Bat Algorithm was applied to a real distribution network to confirm the algorithm feasibility. Also, the results have proven the accuracy of the proposed method.

The paper presents a fuzzy approach for evaluation of the power/energy losses in transformers from the electrical distribution networks considering three input variables: the loading factor, the voltage on low side, and the performance standard. These are modelled as linguistic variables and fuzzy numbers. The obtained results on a real distribution network have shown that the developed approach can be used as an alternative tool to estimate the power/energy losses in transformers in uncertainty conditions when there is few information about the network state and performance standard.

The paper investigates the power losses for real distribution networks in harmonic polluted state. Actually, the low voltage (LV) distribution networks supply a significant number of nonlinear consumers which causes a distorted and unbalanced state. A negative effect of these states is represented by the increase of power losses in distribution network elements. Even if the symmetrical sinusoidal state is initially considered for the power losses, in reality this often leads to power lines overloading, especially for the neutral. The method for power losses estimation in LV distribution network in harmonic polluted environment is very difficult to use. The aim of this paper is to present a reasonably accurate approach to evaluate the effects of harmonic currents on power losses for LV distribution networks using fuzzy techniques. Moreover, to highlight the proposed methodology, a study case for a real low voltage distribution network in order to compute the additional power losses due to the distorted state is provided.