Hermen Toersche

Integration of heat pumps in distribution grids: Economic motivation for grid control

Electric heat pumps combined with heat buffers are important elements in smart grids since they together allow to shift the consumption of electricity in time. In this paper the effects of different control algorithms for heat pumps on the investment costs for distribution grids are investigated. For this, an optimization approach is implemented for a case study within an area where the buildings are only supplied by electricity. The simulations use real smart meter data to generate realistic load curves of households and heat pumps. The calculations show that grid costs increase up to 71% with an inappropriate control and decrease by 10% with an optimal integration of heat pumps. Furthermore, the costs for the reinforcement of the grid are confronted with the benefits on consumer side using flexible price signals. The cost-benefit analysis shows that considering grid restrictions in the context of controllable devices is highly recommended.

Demand side management using profile steering

Many Demand Side Management (DSM) approaches use energy prices as steering signals. This paper shows that such steering signals may result in power quality problems and high losses. As an alternative, this paper proposes to use desired (e.g., flat) power profiles as steering signals and presents an efficient scheduling algorithm that can follow desired power profiles. This paper investigates the complexity of price and profile steering, and presents an algorithm for profile steering. The evaluation of this algorithm studies the results of a best possible uniform pricing and profile steering for a case of 121 houses, each with an electrical vehicle of which the power consumption can be controlled and shifted in time. In contrast to the other evaluated approaches, our profile steering algorithm results in a much flatter profile and keeps the voltage between 220V and 235V at each node. It reduces distribution losses by 57% compared to no control, and by 48% compared to uniform pricing.

Controlling the heating mode of heat pumps with the TRIANA three step methodology

Heat pump based heating systems are increasingly becoming an economic and efficient alternative for domestic gas heating systems. Concentrations of heat pump installations do consume large amounts of electricity, causing significant grid distribution and stability issues when the diversity factor is low. In this work, the three step control methodology TRIANA is extended to support the control of a heat pump fleet in order to improve diversity. Simulations show that TRIANA can reduce the peak load by at least 25% and improve σ by 33% for a representative soil-water scenario. Mathematical optimization shows that further improvement is possible.

Energy management with TRIANA on FPAI

The current growth of smart grid capable appliances motivates the development of general and flexible software systems to support these devices. The FlexiblePower Application Infrastructure (FPAI) is such a system, which classifies devices by their type of flexibility. Subsequently, energy applications only have to support these flexibility classes. In this work, we present an implementation of the TRIANA demand side management approach as an energy application on the FPAI energy management software platform. We use dynamic programming to solve the local scheduling problems for each flexibility class. This work shows that FPAI can host energy applications with different control approaches and that the TRIANA control approach can be embedded in a general implementation framework.

Controlling smart grid adaptivity

Methods are discussed for planning oriented smart grid control to cope with scenarios with limited predictability, supporting an increasing penetration of stochastic renewable resources. The performance of these methods is evaluated with simulations using measured wind generation and consumption data. Forecast errors are shown to affect worst case behavior in particular, the severity of which depends on the chosen adaptivity strategy and error model.

Cascaded column generation for scalable predictive demand side management

We propose a nested Dantzig-Wolfe decomposition, combined with dynamic programming, for the distributed scheduling of a large heterogeneous fleet of residential appliances with nonlinear behavior. A cascaded column generation approach gives a scalable optimization strategy, provided that the problem has a suitable structure. The presented approach extends the TRIANA smart grid framework for predictive demand side management; the main goal of this framework is peak shaving. Simulations validate that the approach is effective, but also show that the performance degrades for smaller group sizes.

Column generation based planning in smart grids using TRIANA

Column generation is applied to the TRIANA smart grid frameworks planner and combined with distributed dynamic programming. Simulations show that this improves the resulting planning compared to methods described in previous work (extra peak reduction of 7.3%; large fluctuation reduction) at the cost of additional processing.