Benjamin Biegel

Congestion management in a smart grid via shadow prices

Abstract We consider a distribution grid interconnecting a number of consumers with flexible power consumption. Each consumer is under the jurisdiction of exactly one balancing responsible party (BRP), who buys energy at a day-ahead electricity market on behalf of the consumer. We illustrate how BRPs can utilize the flexibility of the consumers to minimize the imbalance between the consumed and the purchased energy thereby avoiding trading balancing energy at unfavorable prices. Further we show how shadow prices on the distribution lines can be used to resolve grid congestion without information sharing between the BRPs.

Primary Control by ON/OFF Demand-Side Devices

We consider an aggregator managing a portfolio of ON/OFF demand-side devices. The devices are able to shift consumption in time within certain energy limitations; moreover, the devices are able to measure the system frequency and switch ON and OFF accordingly. We show how the aggregator can manage the portfolio of devices to collectively provide a primary reserve delivery in an unbundled liberalized electricity market setting under current regulations. Furthermore, we formulate a binary linear optimization problem that minimizes the aggregators cost of providing a primary reserve delivery of a given volume, and demonstrate this method on numerical examples.

Indirect control for demand side management - A conceptual introduction

The concept of “indirect control” has become an relevant discussion term in relation to activation distributed and small-scale demand and generation units to provide resources for power system balancing. The term and its association with price signals has, however caused some confusion as to its correct definition, either as a control or a market concept. This paper aims to provide a conceptual introduction to “indirect control” for management of small and distributed demand side resources. A review of control concepts and an analysis of “indirectness” features are provided to create a framework for systematic classification of indirect control strategies. The concepts developed then enable a discussion of control performance and valuation of direct- and indirect control strategies.

Distributed low-complexity controller for wind power plant in derated operation

We consider a wind power plant of megawatt wind turbines operating in derated mode. When operating in this mode, the wind power plant controller is free to distribute power set-points to the individual turbines, as long as the total power demand is met. In this work, we design a controller that exploits this freedom to reduce the fatigue on the turbines in the wind power plant. We show that the controller can be designed in a decentralized manner, such that each wind turbine is equipped with a local low-complexity controller relying only on few measurements and little communication. As a basis for the controller design, a linear wind turbine model is constructed and verified in an operational wind power plant of megawatt turbines. Due to limitations of the wind power plant available for tests, it is not possible to implement the developed controller; instead the final distributed controller is evaluated via simulations using an industrial wind turbine model. The simulations consistently show fatigue reductions in the magnitude of 15-20%.

Smart grid dispatch strategy for ON/OFF demand-side devices

We consider an aggregator managing a portfolio of runtime and downtime constrained ON/OFF demand-side devices. The devices are able to shift consumption in time within certain energy limitations. We show how the aggregator can manage the portfolio of devices to collectively provide upward and downward regulation. Two control strategies are presented enabling the portfolio to provide regulating power while respecting the runtime, downtime, and energy constraints of the devices. The first strategy is a predictive controller requiring complete device information; this controller is able to utilize the full flexibility of the portfolio but can only handle a small number of devices. The second strategy is an agile controller requiring less device information; this controller is able to handle a large number of devices but not able to utilize the full flexibility of the portfolio.

Wind turbine pitch optimization

We consider a static wind model for a three-bladed, horizontal-axis, pitch-controlled wind turbine. When placed in a wind field, the turbine experiences several mechanical loads, which generate power but also create structural fatigue. We address the problem of finding blade pitch profiles for maximizing power production while simultaneously minimizing fatigue loads. In this paper, we show how this problem can be approximately solved using convex optimization. When there is full knowledge of the wind field, numerical simulations show that force and torque RMS variation can be reduced by over 96% compared to any constant pitch profile while sacrificing at most 7% of the maximum attainable output power. Using iterative learning, we show that very similar performance can be achieved by using only load measurements, with no knowledge of the wind field or wind turbine model.

Model predictive control of domestic heat pump

In this paper we consider a house heated by a domestic heat pump. We estimate the economical savings that can be achieved by moving the electrical heat pump load in time according to the time-varying value of electricity. Real data from an inhabited typical Danish house is used to estimate a simple thermal house model. Based on this model and based on the electricity spot prices, we design a control algorithm that seeks to minimize the cost of house heating. Via simulations on a model of a typical Danish single-family house, we show that economical savings in the magnitude of 7 % can be achieved during winter by allowing slightly higher variations in the indoor temperature. Further we show that these economical savings are upper bounded by 12 % for the particular case.

Aggregation and Control of Flexible Consumers: A Real Life Demonstration

Abstract In this paper, we present an architecture for aggregation and control of a portfolio of flexible consumers. The architecture makes it possible to control the aggregated consumption of the portfolio to follow a power reference while honoring local consumer constraints. Hereby, an aggregator is able to utilize a portfolio of consumers as a virtual power plant to deliver services in the electricity markets. The architecture is implemented and demonstrated in a field test on a portfolio consisting of 54 heat pumps each located in an inhabited household. In this demonstration, a power reference varying between 15 kW and 35 kW is followed over a 7 day period. The field test showed satisfactory performance in terms of following the power reference and assuring comfort for the inhabitants. To the best knowledge of the authors, this is the first real life demonstration where a power reference is followed based on the aggregated consumption of a larger number of devices – and consequently a significant step towards the smart grid vision.

Aggregation and Control of Supermarket Refrigeration Systems in a Smart Grid

Abstract In this work, control strategies for aggregation of a portfolio of supermarkets towards the electricity balancing market, is investigated. The supermarkets are able to shift the power consumption in time by pre-cooling the contained foodstuff. It is shown how the flexibility of an individual supermarket can be modeled and how this model can be used by an aggregator to manage the portfolio to deliver upward and downward regulation. Two control strategies for managing the portfolio to follow a power reference are presented and compared. The first strategy is a non-convex predictive control strategy while the second strategy consists of a PI controller and a dispatch algorithm. The predictive controller has a high performance but is computationally heavy. In contrast the PI/dispatch strategy has lower performance, but requires little computational effort and scales well with the number of supermarkets. Two simulations are conducted based on high-fidelity supermarket models: a small-scale simulation with 20 supermarkets where the performance of the two strategies are compared and a large-scale simulation with 400 supermarkets which only the PI/dispatch controller is able to handle. The large-scale simulation shows how a portfolio of 400 supermarkets successfully can be used for upward regulation of 900 kW for a two hour period.

Electricity market optimization of heat pump portfolio

We consider a portfolio of domestic heat pumps controlled by an aggregator. The aggregator is able to adjust the consumption of the heat pumps without affecting the comfort in the houses and uses this ability to shift the main consumption to hours with low electricity prices. Further, the aggregator is able to place upward and downward regulating bids in the regulating power market based on the consumption flexibility. A simulation is carried out based on data from a Danish domestic heat pump project, historical spot prices, regulating power prices, and spot price predictions. The simulations show that electricity price reductions of 18-20% can be achieved compared to the heat pumps currently in operation.