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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 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 global climate change mitigation efforts have increased the efforts of national governments to incentivize local households in adopting PV panels for local electricity generation. Since PV generation is available during the daytime, at off-peak hours, the optimal management of such installations often considers local storage that can defer the use of local generation to a later time. The energy stored in batteries located in optimal places in the network can be used by the utility to improve the operation conditions in the network. This paper proposes a metaheuristic approach based on a genetic algorithm that considers three different scenarios of using energy storage for reducing the energy losses in the network. Two cases considers the battery placement and operation under the direct control of the network operator, with single and multiple bus and phase placement locations. Here, the aim was to maximize the benefit for the whole network. The third case considers selfish prosumer battery management, where the storage owner uses the batteries only for their own benefit. The optimal design of the genetic algorithm and of the solution encoding allows for a comparative study of the results, highlighting the important strengths and weaknesses of each scenario. A case study is performed in a real distribution system.

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.

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.

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.

The decision making in the electric distribution systems is based on data collected from consumers and the various measurement points located in the network (transformer substations, supply points, branch points, etc.). The information obtained from a 100% integration of the smart metering to consumers comes to fill the data acquired through the Supervisory Control and Data Acquisition (SCADA) system, so that the Distribution Network Operator (DNO) can accurately estimate the state of the supervised system. However, the implementation of smart metering is in various implementation stages in different countries of the world, so that today it can be stated that there is no complete integration. The same aspect should be emphasized in the case of the SCADA system at the level of distribution networks, which is not 100% integrated in the low/medium voltage electric substations. In these conditions, the DNO should apply the mathematical tools that take into account the similarities between the consumers’ behaviour and respectively the structure of the load supplied from the electric substations. The regression analysis-based approaches for the load modelling from the nodes of electricity distribution networks were treated in the chapter. The approaches refer to the estimation of the required powers in the supply points with a mixt structure of the load (i.e. residential, commercial, and industrial) at the hour when the maximum value of the load is recorded and the demand of residential consumers which represent the highest percentage from the load structure fed from the electric substations. The proposed approaches were tested in real operation conditions of the distribution networks from Romania.

The overhead high-voltage power lines (OHVPLs) are considered significant sources of extremely low frequency (ELF) electric and magnetic fields (EMFs), whose potential health effects became during the past decades a matter of scientific debate and public concern all over the world. In this chapter, a simple and yet effective finite element (FE) approach is proposed to compute and analyze—from the perspective of public exposure—both electric and magnetic fields emitted by typical configurations of OHVPLs belonging to the Romanian power grid. First, a 2D ANSYS Maxwell model is developed for the specific instance of a 110 kV double-circuit OHVPL and validated against two software tools based on quasi-static analytical methods, PowerELT and PowerMAG. Next, it will be used to investigate exposure to ELF-EMFs emitted by a selection of OHVPLs with nominal voltages of 110 kV, 220 kV and 400 kV, taking into consideration influencing factors such as loading, phasing and ground clearance. Compliance with the exposure guidelines specified by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) for general public is assessed for each particular case. As a result, all calculated magnetic fields are below the ICNIRP limit of 100 μT, while the electric fields exceed the ICNIRP limit of 5000 V/m only in limited areas beneath the 400 kV OHVPLs. The calculated field levels are in line with those reported in the scientific literature for similar OHVPLs.

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.

The deregulated markets have replaced the traditional way of trading electricity from the producers to the consumer, via government-owned organizations and regulated tariffs. Nowadays, electricity prices are determined by the offer-demand mechanism and consumers can negotiate tariffs with their supplier of choice. For classic wholesale suppliers, the tariffs are a result of the transactions performed on the wholesale market and the energy mix available in certain geographical regions. In parallel with becoming eligible and participating in retail electricity markets, the consumers use increasingly local generation sources based mostly on renewable energy technologies such as 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. Currently, several trading mechanisms for prosumers are available, ranging from the simplest, selling back the surplus to an aggregator at fixed tariffs, to more complex market schemes. This paper proposed a two-tier local market model for prosumers and consumers connected in microgrids, based on the blockchain technologies and other technologies and concepts such as remote sensing, smart grids, crowdsourcing and energy poverty.

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.

Four-wire low-voltage microgrids supply one-phase consumers with electricity, responding to a continuously changing demand. For addressing climate change concerns, national governments have implemented incentive schemes for residential consumers, encouraging the installation of home PV panels for covering self-consumption needs. In the absence of adequate storage capacities, the surplus is sold back by these entities, called prosumers, to the grid operator or, in local markets, to other consumers. While these initiatives encourage the proliferation of green energy resources, and ample research is dedicated to local market designs for prosumer–consumer trading, the main concern of distribution network operators is the influence of power flows generated by prosumers’ surplus injection on the operating states of microgrids. The change in power flow amount and direction can greatly influence the economic and technical operating conditions of radial grids. This paper proposes a metaheuristic algorithm for prosumer surplus management that optimizes the power surplus injections using the automated control of three-phase inverters, with the aim of reducing the active power losses over a typical day of operation. A case study was performed on two real distribution networks with distinct layouts and load profiles, and the algorithm resulted efficient in both scenarios. By optimally distributing the prosumer generation surplus on the three phases of the network, significant loss reductions were obtained, with the best results when the generated power was injected in an unbalanced, three-phase flow.

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.

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.

In the last years, in the operation of electric distribution grids (EDGs), the optimization process is reduced to small size grids (namely microgrids) for which the number of variables is much lower, and finding the optimal solution is not a problem. To better supervise and control each microgrid, the emergence of smart metering system (SMS) can be interpreted as a transformation in progress encountered at the level of majority of distribution grid operators (DGOs). In these conditions, distribution grid operators (DOGs) have possibility to obtain online data about the electricity consumption of customers, respectively the electricity amounts product by the renewable sources, which allows them to take some technical measures which enable the microgrids to operate more energy efficient and better plan their investments. A critical assessment of microgrids referring to improving the energy efficiency brings out a series of problems partially unresolved due to the particularities of microgrids where they have been implemented. These problems are studied in this chapter and SMS-based new solutions are proposed for load modelling, phase load balancing and voltage control. All approaches are tested using real microgrids, and the obtained results highlight the performances on the energy efficiency measures.

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 %.

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 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.

Voltage quality is an essential requirement in the operation of electricity distribution systems. At consumer busbars, the deviation from the nominal voltage has specific ranges, prescribed in technical regulations. A widely chosen voltage correction approach is to use reactive power compensation. Capacitor banks are used for this purpose in highly loaded networks, but their placement and sizing require optimization. Metaheuristic methods are frequently used for parallel optimization problems, and the Genetic Algorithm (GA) is a very well-known metaheuristic technique. However, the performance of the GA is highly dependent on the settings chosen by the user. This paper investigates the influence of the crossover type used on the results of the GA applied to the problem of voltage profile improvement using reactive power compensation with capacitor banks in electricity distribution networks.

The optimal capacitor allocation in electricity distribution networks (EDN) plays a meaningful role in voltage profile improvement, power factor amelioration and also power losses minimization. This work presents two comprehensive optimization algorithms based on metaheuristics as a comparison to solve the capacitor allocation problem in modern distribution networks. Thus, the problem of optimal capacitor allocation for active power losses minimization was solved using the bat and fireworks algorithms. In order to validate and demonstrate the feasibility of the proposed approach, a modern distribution network was tested and the results obtained in MATLAB implementations of the two proposed metaheuristics were compared. The results confirm that the proposed optimization algorithms show a good efficiency and robustness having high performance both for minimizing the EDN power losses and voltage profile improvement.

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.

Capacitor banks (CBs) are used for reactive power compensation in electricity distribution systems. In order to achieve the best effect, CBs placement requires optimization. Metaheuristic computational intelligence algorithms are methods widely used in the literature for this purpose. This paper presents a parallel Particle Swarm Optimization algorithm for bus voltage optimization. The results of a study case show the superiority of the parallel approach over the individual PSO.

The paper presents the results of a new statistical based data mining approach that explores the load curves in order to detect abnormal data, the so-called outliers. The load curves characterized by their main indicators are provided from Smart Meters mounted on the power substation. The proposed method was tested using a real database with 290 substations from rural area. By outlier's elimination, the results demonstrate the ability of the proposed approach to be efficiently used by distribution operators in accurate patterns discovery of the load curves characteristics with the help of which the operation and planning of power systems, could be made.

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.

Important changes that occurred during the last years regarding the shifting of the consumption share from high voltage (HV) to medium voltage (MV) and low voltage (LV), the diversification and the increase of the number of non-linear and/or non-symmetrical consumers, the removal of the reactive power sources (shunt capacitor banks — SCB) that contained polychlorinated biphenyl compounds (PBC) placed in the electricity repartition and distribution systems (ERDS), etc., led to the appearance of some complex problems related to the maintaining of the quality of electricity delivered to the consumers through ERDS. Using multi-criteria optimization processes, the paper presents mathematical models, algorithms and computation software intended for the best management of the reactive power sources, both in the nodes of the electricity repartition systems (ERS) and the electricity distribution systems (EDS). Thus, either traditional algorithms or algorithms based on possibilities offered by artificial intelligence are used.

At present conditions on our country, the monitoring of consumers supplied from the electricity distribution networks represents an essential instrument for substantiation of the infrastructure development strategies of electricity distribution operators. A better understanding of the electricity consumption characteristics of different consumers, the structure and operating state optimization of public distribution networks corroborated with the quality improvement of the supply service must lead to an adequate management of electricity consumption. Thus, a complex consumption monitoring system (load curves of the consumers and the main indicators that characterize these load curves) is created and presented in the paper, a system based on Alpha three-phase counters. In order to test this complex monitoring instrument, at the paper end, are presented the extracted and processed results with the help of the monitoring system specialized application at a tertiary consumer (hotel), supplied from the public electricity distribution networks.

The electricity repartition public systems from our country are made up of 110 kV overhead and cable lines that usually operate in normal steady state in complex loop configuration, as well as 110 kV/MT step-down stations. Under these conditions, the active power and energy losses computation or assessment in operation must be done simultaneously on all components due to the fact that their operating states are inter-conditioned. The level of these losses is generally influenced both by the system configuration and the consumers operating state, as well as the loads of different power sources that discharge in the system, such as, for example, alternators from the power stations, reactive power sources, etc. For the above-mentioned reasons, best results regarding the active power and energy losses estimation are obtained through repeated steady state computation for a necessary and sufficient number of levels for the characterization as complete as possible of the system operation, taking both into account the consumption variation and power sources that inject energy in system. For these purposes, within the paper, for an existent and extended repartition public system in complex loop configuration, the evaluation possibilities are analyzed with best precision regarding active energy losses, through repeated steady state computation, the load curves being split into 24 hourly levels.