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This paper provides a hierarchical optimization algorithm based on the inverse simulation for the exploitation of the flexibility of electrothermal heating systems in office buildings in a demand response schema led by an aggregator. Inverse simulation, based on the approximate differentiation method, is applied on local building level to turn the customer's comfort specifications, i.e., indoor temperature comfort zone, into the technical power and flexibility specifications and, hence, acceptable power schedules. The aggregator then optimizes for a global system level objective. We prove that the inverse simulation yields the same result as the forward simulation approach, whereas it protects the privacy and thermal comfort of the customer. Moreover, via a general use case, we provide an insight on how to optimally balance demand and supply on the system level.

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.

This paper analyzes and compares one central and one distributed optimization method applied to complex city districts embedded in a hierarchical architecture. We define a complex city district by the coexistence of residential, nonresidential, and industry. The proposed methods are for offline, day-ahead optimization for operation planning purposes of, e.g., electrothermal heating systems. This paper analyzes how this way city districts may provide flexibility for demand response purposes. The optimization objective is the matching of supply and demand on system level by maximizing the usage of renewable energy generation. We provide quantitative and qualitative results to compare the performance of the algorithms and their potential for providing flexibility.

This paper presents a hierarchical robust distributed optimization for day-ahead and intra-day scheduling of the operation of flexible devices (electro-thermal heating units) within a city district. An aggregation service provider, which acts as an aggregator, performs this distributed optimization to maximize the flexibility potential of its customers to provide services to other actors, such as to a balance responsible party or the distribution system operator. Our optimization algorithm is based upon the alternating direction method of multipliers and prioritizes each individual customer and its own private objective. A model predictive control and robust design guarantee that uncertainty, e.g., electrical or thermal demand, is managed within the optimization process. The work includes a customer versus system level objective (aggregator) analysis under uncertainty.

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.

This paper describes a distributed optimization algorithm for scheduling the operation of electro-thermal heating units, such as heat pumps (HP) or electric heaters (EH) within a day-ahead (DA) operation for covering space heating demand. Flexibility for Demand Side Management (DSM) is realized through thermal storage on building level. The distributed optimization is performed offline by using the Alternating Direction Method of Multipliers algorithm (ADMM). For the distributed optimization also a suitable architecture is adapted and described. The optimization algorithm is tested within an example city district scenario and evaluated according to defined metrics.

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.

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.

Flexibility on the demand side helps to integrate renewable generation systems into the energy system. Flexibility can be provided by storage systems as well as by controllable loads and generation units. Battery systems with a first life in electric vehicles can be used as second life batteries in stationary storage systems to increase the profitability of the system. This paper introduces a district with a high potential to provide flexibility. The district comprises a wind turbine and three buildings, two of which can provide flexibility thanks to the combination of the installed electro-thermal units and a second life battery system. Therefore, the district is able to provide different services like demand response or ancillary services to the DSO. The objective of this paper is to identify the services that the district can provide and to investigate the requirements to the district defined by such services.