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The advantage of a Wireless Sensor Network (WSN) compared to a centric approach is the distribution of sensing suites. However, in order for such a system of distributed resources to work in a reliable and effective way a smart cooperation between nodes is needed. In this paper we propose a middleware approach for a highly reliable data storage that helps to assure data availability despite the well known WSN resource problems and disappearing or inactive nodes by providing a reasonable data redundancy in the system. Such a solution helps to ease the design and optimization of the data exchange between nodes as well. Our solution is configurable in order to satisfy the needs of the application on top regarding performance/requirements trade-off. The options specify the quantity and quality of the data replication. Additional features like event mechanism that monitors the data and the possibility to issue database like queries increase the applicability of our middleware. In this paper we focus on the evaluation of its capabilities regarding reliability, the consistency of replicates and the costs of the data management. The simulation results for a reasonable set-up show that the CPU load caused by the data replication is low (below 3 percent) and the average inconsistency time is as small as about 0,06 seconds for a single hop and about 0,15 seconds for a two hops replication area. There is still room for improvements, but a clear definition of problems helps to find ways to cope with them in order to achieve the chosen goals.

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Currently, ensuring the correct functioning of the electrical grid is an important issue in terms of maintaining the normative voltage parameters and local line overloads. The unpredictability of Renewable Energy Sources (RES), the occurrence of the phenomenon of peak demand, as well as exceeding the voltage level above the nominal values in a smart grid makes it justifiable to conduct further research in this field. The article presents the results of simulation tests and experimental laboratory tests of an electricity management system in order to reduce excessively high grid load or reduce excessively high grid voltage values resulting from increased production of prosumer RES. The research is based on the Elastic Energy Management (EEM) algorithm for smart appliances (SA) using IoT (Internet of Things) technology. The data for the algorithm was obtained from a message broker that implements the Message Queue Telemetry Transport (MQTT) protocol. The complexity of selecting power settings for SA in the EEM algorithm required the use of a solution that is applied to the NP difficult problem class. For this purpose, the Greedy Randomized Adaptive Search Procedure (GRASP) was used in the EEM algorithm. The presented results of the simulation and experiment confirmed the possibility of regulating the network voltage by the Elastic Energy Management algorithm in the event of voltage fluctuations related to excessive load or local generation.

Ensuring flexibility and security in power systems requires the use of appropriate management measures on the demand side. The article presents the results of work related to energy management in households in which renewable energy sources (RES)can be installed. The main part of the article is about the developed elastic energy management algorithm (EEM), consisting of two algorithms, EEM1 and EEM2. The EEM1 algorithm is activated in time periods with a higher energy price. Its purpose is to reduce the power consumed by the appliances to the level defined by the consumer. In contrast, the EEM2 algorithm is run by the Distribution System Operator (DSO) when peak demand occurs. Its purpose is to reduce the power of appliances in a specified time period to the level defined by the DSO. The optimization tasks in both algorithms are based on the Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic algorithm. The EEM1 and EEM2 algorithms also provide energy consumer comfort. For this purpose, both algorithms take into account the smart appliance parameters proposed in the article: sections of the working devices, power reduction levels, priorities and enablingof time shifting devices. The EEM algorithm in its operation also takes into account the information about the production of power, e.g., generated by the photovoltaic systems. On this basis, it makes decisions on the control of smart appliances. The EEM algorithm also enables inverter control to limit the power transferred from the photovoltaic system to the energy system. Such action is taken on the basis of the DSO request containing the information on the power limits. Such a structure of EEM enables the balancing of energy demand and supply. The possibility of peak demand phenomenon will be reduced. The simulation and experiment results presented in the paper confirmed the rationality and effectiveness of the EEM algorithm.