Olli Kilkki

Optimized Control of Price-Based Demand Response With Electric Storage Space Heating

The increased uncertainty of the electric grid due to the penetration of renewable energy sources and deregulation of the electric market is aimed to be alleviated by demand response (DR) in the future smart grid. The demand-side resources can be incentivized to alter their consumption patterns by varying their electricity price over time. A major residential energy demand contribution is from electric heating, which, when combined with smart energy storage using water heaters, could be utilized to defer consumption to more inexpensive periods without affecting the customers thermal quality of service. The objective is to optimize the consumer electricity price of electric storage space heating customers, in order to maximize the profit of the retailer. This approach of varying the customer electricity prices leads to a game-theoretic scenario, where the procurement and consumption profiles of the retailer and consumer agents are based on the set electricity price. The optimization of the consumer electricity price is shown to offer lesser expense for the retailer. In addition, hourly load-following can be improved by offering further discounts for the consumers.

Agent-based modeling and simulation of a smart grid: A case study of communication effects on frequency control

Abstract A smart grid is the next generation power grid focused on providing increased reliability and efficiency in the wake of integration of volatile distributed energy resources. For the development of the smart grid, the modeling and simulation infrastructure is an important concern. This study presents an agent-based model for simulating different smart grid frequency control schemes, such as demand response. The model can be used for combined simulation of electrical, communication and control dynamics. The model structure is presented in detail, and the applicability of the model is evaluated with four distinct simulation case examples. The study confirms that an agent-based modeling and simulation approach is suitable for modeling frequency control in the smart grid. Additionally, the simulations indicate that demand response could be a viable alternative for providing primary control capabilities to the smart grid, even when faced with communication constraints.

MAS-Based Modeling of Active Distribution Network: The Simulation of Emerging Behaviors

Agent-based modeling of active distribution network helps to understand the dynamics and to design the control strategies for overall system efficiency. There is, however, a lack of generic and multipurpose agent definitions in existing studies. In this paper, a multi-agent system-based modeling of an active distribution network is presented using cooperative agents. A method to solve a network-wise objective of state estimation is explained with the proposed model. The network component agents are defined to be cooperative to meet the overall objectives and greedy to fulfil individual objectives such as energy cost minimization. A token-ring protocol is deployed for the agent communication among themselves, as well as with market and network operator agents. Furthermore, a MATLAB/Simulink model of active distribution network is used to simulate the emerging stochastic loading scenario, while the autonomous prosumer agents optimize their total energy cost responding to market price variations.

Optimization of decentralized energy storage flexibility for frequency reserves

This paper presents a cost optimization scheme for an electricity aggregator. The aggregator schedules the charging of the energy storages of its aggregated group of consumers to minimize the hourly spot market costs, while simultaneously maximizing its potential for participation in the reserve market. The optimization is formulated as a distributed iterative algorithm and its performance on the day-ahead and regulating power markets are evaluated based on simulations using actual market data. It was shown that increasing the consumption flexibility increased the overall profits markedly, while increasing their variance, up to a point.

A Distributed ICT Architecture for Continuous Frequency Control.

The active participation of consumers in frequency control can mitigate the negative effects of variable renewable generation in a power system. This study aims at designing a distributed informati ...

Towards a task allocation algorithm for frequency containment reserves

The negative effects of variable renewable generation in the power systems requires the engagement of the demand-side. In fact, consumers-owned energy resources can be engaged to provide the flexibility to the power grid, thus increasing its reliability. This study presents an algorithm for allocating tasks to distributed consumer owned energy resources, to enable consumers to participate in the automated demand response for frequency control by providing reserves for the frequency containment reserves market. The task allocation algorithm is specified as a set of procedures enabling the coordination of energy resources. In addition, a prototype implementation of the automated demand response system is utilized to evaluate the performance of the task allocation algorithm through simulations. The simulation results shows that the designed algorithm achieves the specified objectives, and fulfills the requirements specified by the transmission system operator for the participation in the provision of frequency containment reserves.

Incentives for shaping the consumption profile of a group of consumers with energy storage devices

The effects of increasing volatility of energy production could be mitigated by real-time shaping of the electricity consumption profile. This paper compares various strategies of an electricity retailer for controlling the consumption profile of its customers, under uncertainties in wholesale prices as well as heating demand. The economic and technical performance potential of selected forms of direct load control and dynamic pricing contracts are assessed through numerical simulations involving electric storage space heating consumers. Best potential monetary benefits were obtained by allowing direct load control and optimizing for additional flexibility, when simulating trading in the intra-day market.

Optimization of control transmissions by event-driven model prediction

In this paper we study the optimization of control transmissions in event-driven control systems, with respect to the control performance and number of communication events. The control signal transmissions are decided based on optimization in a model predictive framework, called Model Predictive Triggering (MPT). MPT is developed because the typical send-on-delta approach does not take into account control performance. Several transmission cost functions criteria are compared. We show and compare the performance of the different alternatives using simulation, including a conventional model predictive control approach. The MPT method is shown to be the effective considering the transmission count and control performance, with tuning parameters to select a suitable trade-off between communication and control.