Electricity, Volume 3, Issue 4 (December 2022) – 5 articles

This paper presents a stochastic approach to assessing the hosting capacity for solar PV. The method is part of the optimal techniques for the integration of renewables. There are two types of uncertainties, namely aleatory and epistemic uncertainties. The epistemic and aleatory uncertainties influence distribution networks’ hosting capacity differently. The combination of the two uncertainties influences the planning of distribution networks. The study introduces and considers the epistemic probability distribution function (PDF). DSO does take levels of risk for a parameter violation when planning. Epistemic PDF is a range of values of the planning risk margin for quantifying the hosting capacity. The planning risk acknowledges that overvoltages may occur at weaker conceivable locations in a distribution network. In the paper, it has been shown that the number of customers who will be able to connect solar PV in future is influenced by the DSO’s planning risk margin. The DSO can be stricter or less strict in planning risk margin. It has been concluded that fewer customers can connect solar PV to a distribution network when a DSO takes a stricter planning risk. Alternatively, more customers can connect solar PV units for a less strict planning risk. How stricter or less strict the DSO is with the planning risk margin determines the investment needed for mitigation measures. The mitigation measures in the future will lead to not exceeding the overvoltage limit when solar PV is connected to the weaker conceivable points of the distribution network. Full article

The actual research in terms of energy focuses drastically on the use of green energy resources. Hydropower systems have been the most known green sources for years. However, the hydropower systems, which are seasonal and most exploited, do not cover the speed of increasing daily demand. The injection of solar power could be a supporting alternative, but it is only in daylight, weather dependent and intermittent. Therefore, a storage system is required. The batteries are the quick recourse. Not only the energy sector, but also the transport systems are not left behind; they are striving to turn green. Therefore, they are turning to electric vehicles (EVs) and electric moto-bicycles (EMBs). On the other hand, this option tends to be a sharply increasing demand that can be a burden to the grid, i.e., the increase in the EVs and EMBs implies increases in power demand, grid components and pressure on the grid. Fortunately, the EVs use batteries to store energy for their use. Therefore, the EVs are the power storage system, they become part of the power management system and they can save the power surplus. With the injection of PV solar power, there is no need for an extra storage system, as the EVs are charged from the grid and store the solar energy that can be used later after sunset. The bi-directional off-board charger is a solution as it allows the grid to charge the vehicle (G2V) and the vehicle to send power back to grid (V2G). The inclusion of EVs in power management introduces the concept of vehicle-to-vehicle (V2V) when one EV can charge another, and the vehicle-to-load (V2X) where the EV can supply power to EMBs or any load. The V2G, G2V, V2X, the inclusion on solar energy to the grid and the behavior of the grid in that scenario will be illustrated in this paper. Full article

The operation of electrical networks, microgrids, or heterogeneous battery systems, especially the dispatch of single units within the system, requires sophisticated power flow control strategies. If objectives such as efficiency are demanded for the operation of the energy system, typical control strategies lack the ability to verify the optimality of the operation. Dynamic programming is a widely used method for determining the global optima of trajectory problems. In the context of energy systems and power flow optimization, it is restricted to applications with a low number of states and decisions. The reason for this is the rapid growth of computational effort with increasing dimensionality of the state and decision space. The approach of iterative dynamic programming (iDP) makes dynamic programming applicable to the planning and benchmarking of complex power flow optimization problems. To illustrate this, a heterogeneous battery energy storage system is introduced for which the iDP optimizes the power split at the point of common coupling to minimize the total cumulative loss of energy. The method can be adopted for a broad range of energy systems such as microgrids, utility grids, or electric vehicles. The applicability is limited only by the computation time, which depends on the model complexity and the length of the time series. To verify the functionality of the iterative dynamic programming, its results are directly compared to those of the standard dynamic programming. The total computation time can be reduced by 98% in the tested scenario. As relevant use cases, static and dynamic methods of power sharing are validated and benchmarked. The iDP offers a novel and computationally efficient method for the design and validation of power flow control strategies. Full article

With the evolution of electromobility and heat pumps in urban areas, distribution system operators find themselves facing new challenges in reinforcing their grids. With this evolution, the power demand is developing rapidly and grid reinforcement is urgently needed. The electromobility and heat pump loads are introduced by giving the assumed development scenarios in Germany and their corresponding nominal power assumptions. Furthermore, a method for load modeling in grid planning is explained. Subsequently, several grid planning approaches are presented while dividing them into conventional and innovative planning strategies. Among the investigated innovative planning strategies are three variants of load management that regulate different load types. By analyzing several urban medium voltage grids, this contribution deduces a solid basis for distribution system operators in the form of planning guidelines. The implemented grid planning method leading to the planning guidelines is presented in detail along the contribution. Full article

Accurate electricity demand forecasting is vital to the development and evolution of smart grids as well as the reinforcement of demand side management strategies in the energy sector. Since this forecasting task requires the efficient processing of load profiles extracted from smart meters for large sets of clients, the challenges of high dimensionality often lead to the adoption of cluster-based aggregation strategies, resulting in scalable estimation models that operate on aggregate times series formed by client groups that share similar load characteristics. However, it is evident that the clustered time series exhibit different patterns that may not be processed efficiently by a single estimator or a fixed hybrid structure. Therefore, ensemble learning methods could provide an additional layer of model fusion, enabling the resulting estimator to adapt to the input series and yield better performance. In this work, we propose an adaptive ensemble member selection approach for stacking and voting regressors in the cluster-based aggregate forecasting framework that focuses on the examination of forecasting performance on peak and non-peak observations for the development of structurally flexible estimators for each cluster. The resulting ensemble models yield better overall performance when compared to the standalone estimators and our experiments indicate that member selection strategies focusing on the influence of non-peak performance lead to more performant ensemble models in this framework. Full article