This paper presents a Kalman-filter based maximum power point tracking (KF-MPPT) in a single-stage photovoltaic (PV) system that is connected to the AC grid. In the proposed KF-MPPT method, there isn’t any current sensor on the DC side, so the output power of the PV array isn’t measured directly. This MPPT method uses the d-component of AC current for calculating the maximum powerpoint. The photovoltaic array is connected directly to the power system via a voltage-source inverter (VSI). Hence, the MPPT algorithm must be done by direct controlling of the VSI. For this, an improved voltage-oriented control (VOC) scheme is used, which contains an outer voltage control loop and an inner current control loop. The MPPT algorithm determines the reference voltage of the outer voltage control loop. The inner current control loop is controlled as the system works at the maximum power point, as well as the reactive injected power set to be zero. The performance of the proposed system is validated by using computer simulations and is compared with the improved Perturb and Observe (P&O) method. Simulations results show that the proposed KF-MPPT method provides effective and fast response under rapidly irradiance changes, as well as no oscillation at steady-state.

Continuous access to electricity has become a fundamental demand in the modern world. However, in many countries, the rural areas either have no access to electricity or have access through a weak distribution network with inadequate transmission and distribution infrastructure. On the other hand, higher operational costs, environmental concerns, and challenges pertinent to the fuel management in the systems out of the networks based on gasoline have brought about numerous problems. As a result, making use of alternative energy resources and microgrid systems has captured a great deal of attention. Accordingly, the independent microgrid operators from the network seek appropriate energy management creation to optimize this region's potential to satisfy its energy demands. In this regard, a microgrid containing a photovoltaic generator, diesel generator, wind turbine, and energy storage system has been designed for the Ardabil region in this paper. The installation locations of these systems are selected according to their features. In this microgrid, the energy management has been turned into an optimization problem, and the Extended Artificial Bee Colony Algorithm has been used to solve it. This extended method has been proposed based on the chaos theory and the best response. The obtained results indicate the efficiency of the proposed method.

Due to an increasing share of renewable energy sources the balancing of energy production and consumption is getting a lot of interest considering future smart grids. In this context, many investigations on demand-response programs are being conducted to achieve flexibility from different energy storages and loads. As space heating is an important schedulable load for flexibility simulations, there are different modelling approaches due to its interdisciplinary nature. Models can be built from the civil engineering or electrical engineering point of view, depending on the computational expense and accuracy level. Scheduling optimizations need a lot of simulations, preferably with computationally light models. Thus, this work will use a computationally light neural network load prediction model for space heating which is based on a detailed civil engineering model. Simulations with different scheduling times were conducted to see the long- and short-term effects of the demand response action. Results show, that applying the same demand response action at different times results in different behaviors of the system resp. energy consumption, which requires further studies for developing optimized scheduling methods.

Estonia is among the countries with the most energy-efficient Near Zero Energy Buildings (NZEBs) in Europe, i.e. their energy consumption is the lowest. From the energy management prospects, on one hand, the rising price of electricity and decreasing the feed-in prices, and on the other hand, the decreasing price of battery energy storage system (BESS) have made the self-consumption of NZEBs profitable. This paper develops a simple tool to study the optimal sizing of BESS and increase the profitability of NZEBs. The BESS sizing is done considering the installed PV capacity in the NZEB, the energy consumption profile, and the local electricity prices. The results of this paper demonstrate to the NZEB owner and planers that in case of self-consumption, investment in PV systems could be more profitable when it is combined with BESS. Simulation results are presented to validate the proposed BESS sizing tool and analyze the sensitivity of results to changes in electricity and battery system price.

Due to an increasing use of renewable energy sources in the power grid, it is of high importance to balance supply and demand for grid utilities and microgrid operators. If there are mismatches in the balancing, microgrids with islanded operation capabilities would be preferrable. In islanded mode, nearly zero energy buildings commonly use a stand-alone photovoltaics power supply with a battery storage. A battery storage is expensive and the capacity in case of off-grid operation depends on the electricity consumption of the dwelling's occupants. Using thermostatically controlled appliances like a freezer, water heater and space heating as additional storage systems can reduce the capacity of the battery storage system or increase the operation time in islanded mode for a fixed battery size. This paper analyzes the battery capacity dependency both on the control algorithms for the thermal storages and on the occupancy of the dwelling. Possible battery reductions for different selected occupancies are presented in this work by comparing the simulation results of different load matching algorithms to each other and between the different occupancies. The analysis of those results enables recommendations on the most suitable algorithm for most occupancy scenarios of an existing dwelling with respect to a minimized battery capacity. This can be particularly useful, for example, for dwelling and apartment owners who are renting out dwellings.