Search

In order to find decisions in finance, regulations, politics including long term strategies for the electricity system and strategic planning on the industrial side, a holistic view on the overall energy system and markets is required at different levels of detail. more...

This chapter introduces the topic of demand side management for an efficient use of flexibility in energy management. It starts with a general introduction about demand side management and flexibility sources in city districts. Afterwards, the general portfolio optimization problem formulation for demand side management is introduced and linked to an architecture. This architecture includes the most relevant actors, such as consumers, an aggregator, and the balance responsible party, and distinguishes between a flexibility provider and a flexibility user. Relevant challenges and requirements for demand side management are discussed and at the end of the chapter, the impact of uncertainty on the optimization via robust or stochastic optimization is addressed. The chapter provides a full day-ahead scheduling example under uncertainty for a city district using the alternating direction method of multipliers as distributed optimization method. more...

This paper describes a modeling and optimization of operation for small compressed-air energy systems (CAES) in the range of kW, typical of small and medium enterprises (SME) and energy intensive industry. These CAES are equipped with air storage. This paper analyzes how those systems may provide flexibility for electrical Demand Response (DR) in city districts. The paper presents first, the modeling of a CAES system. The system design, such as compressor and storage size is then performed based on the modeling results. The mixed-integer linear optimization model for using CAES is explained afterwards. The optimization is an offline day-ahead optimization for planning operation purposes. The approach is analyzed in terms of flexibility within a test case for optimizing the CAES of a typical German car repair shop. more...

This paper introduces a method that deals with Demand Response using a distributed optimization algorithm in a single or multiple building and supplier setup. On the household level, responsive loads are intelligently controlled in order to adapt to renewable power supply. In this paper, heat pump (HP) heating systems, in general one kind of controllable load, are considered as shiftable loads. These systems comprise an electrical heat pump and a thermal storage that can temporarily decouple the electrical demand of the heat pump from the building's thermal demand. Thus, the heat pump load can be shifted with higher flexibility. Wind and solar energy generation or a combination of both is considered as the renewable supplier. The proposed distributed algorithm optimizes the schedules of demand and supply by minimizing the supplier's imbalance costs and the disutility experienced by the customer. Applying Dual Decomposition (DD) leads to the single objectives for supplier and costumer. A global optimum for the overall system is identified through an iterative process based upon the local optima. The algorithm's performance is validated in three different test scenarios including single and multiple buildings as well as suppliers. more...