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.

This paper proposes a method using k-means clustering, based on the operational characteristics (loss sensitivity factor and voltage values in nodes) to determine the optimal location of distributed generation sources into an electrical distribution system. The suggested method is tested on a 20 kV real rural distribution network with 91 nodes. In the optimal nodes from the network analyzed, DG is installed at different power levels, considering the evolution in time of the energy losses. On the basis of the results, the proposed method demonstrates that the methodology can be successfully used to reduce the real power losses, to improve the voltage values in nodes, but more important to determine the optimal placement of DG without violation of any of the system constraints under any operating conditions. The results show the effectiveness of the proposed method.

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.

In electrical distribution systems, except the usual measurements from stations, there are few information about the state of network. As a result, there is at any moment a generalized uncertainty about the power demand conditions and therefore about the network loading, voltage level, power losses, etc. Therefore, it is important to establish accurate the load of nodes from distribution system. An alternative to the settlement based on metered demand is load profile based settlement. In this manner, each node (distribution substation), without appropriate meters, is assigned a representative load profile. For that reason it is necessary to determine typical load profiles. In the first part of the paper, the K-means clustering method is used for typical load profiles determination. In the second part, an improved approach based on the obtained typical load profiles for the load estimation of the electric distribution systems is proposed. To demonstrate the effectiveness of load simulation of the distribution system based on the typical load profiles of the nodes, a practical example is presented.