Self-consumption through power-to-heat and storage for enhanced PV integration in decentralised energy systems

Many countries have adopted schemes to promote investments into renewable energy sources resulting, among others, in a high penetration of solar PV energy. The system integration of the increasing amount of variable electricity generation is therefore a highly important task. This paper focuses on a residential quarter with PV systems and explores how heat pumps and thermal and electrical storages can help to integrate the PV generation through self-consumption. However, self-consumption and PV integration are not only affected by technologies but also by pricing mechanisms. This paper therefore analyses the impact of different tariffs on the investment and operation decisions in a residential quarter and its interaction with the external grid. The considered tariffs include a standard fixed per-kilowatt-hour price, a dynamic pricing scheme and a capacity pricing scheme. To account for the interdependent uncertainties of energy supply, demand and electricity prices, we use a module-based framework including a Markov process and a two-stage stochastic mixed-integer program. Analysing a residential quarter in Southern Germany as a case study, we find that the integration of a PV system is economically advantageous for all considered tariffs. The self-consumption rate varies between 58 and 75%. The largest PV system is built when dynamic prices are applied. However, the peak load from the external grid increases by a factor of two under this tariff without any incentive for reduction. In contrast, capacity pricing results in a reduction of the peak load by up to 35%

Two-stage stochastic, large-scale optimization of a decentralized energy system : a case study focusing on solar PV, heat pumps and storage in a residential quarter

The expansion of fluctuating renewable energy sources leads to an increasing impact of weather-related uncertainties on future decentralized energy systems. Stochastic modeling techniques enable an adequate consideration of the uncertainties and provide support for both investment and operating decisions in such systems. In this paper, we consider a residential quarter using photovoltaic systems in combination with multistage air-water heat pumps and heat storage units for space heating and domestic hot water. We model the investment and operating problem of the quarter’s energy system as two-stage stochastic mixed-integer linear program and optimize the thermal storage units. In order to keep the resulting stochastic, large-scale program computationally feasible, the problem is decomposed in combination with a derivative-free optimization. The subproblems are solved in parallel on high-performance computing systems. Our approach is integrated in that it comprises three subsystems: generation of consistent ensembles of the required input data by a Markov process, transformation into sets of energy demand and supply profiles and the actual stochastic optimization. An analysis of the scalability and comparison with a state-of-the-art dual-decomposition method using Lagrange relaxation and a conic bundle algorithm shows a good performance of our approach for the considered problem type. A comparison of the effective gain of modeling the quarter as stochastic program with the resulting computational expenses justifies the approach. Moreover, our results show that heat storage units in such systems are generally larger when uncertainties are considered, i.e., stochastic optimization can help to avoid insufficient setup decisions. Furthermore, we find that the storage is more profitable for domestic hot water than for space heating

Optimierung der Investitions- und Einsatzplanung dezentraler Energiesysteme unter Unsicherheit

Es wird ein ganzheitliches, modulbasiertes Framework für die Investitions- und Einsatzplanungsoptimierung dezentraler Energiesysteme entwickelt. Mittels stochastischem Programm und Regret-Minimierung werden risikobehaftete und nicht probabilistische Unsicherheiten berücksichtigt. Neu ist auch die parallele Berechnung auf High-Performance-Computing-Systemen einschließlich der eingesetzten automatischen Algorithmuskonfiguration des verwendeten Solvers zur Rechenzeitreduzierung