Claes Sandels

Vehicle to Grid — Monte Carlo simulations for optimal Aggregator strategies

Previous work has shown that it could be profitable on some control markets to use Plug-in Hybrid Electric Vehicles (PHEV) as control power resources. This concept, where battery driven vehicles such as PHEVs provide ancillary service to the grid is commonly referred to as Vehicle to Grid (V2G). The idea is to sell the capacity and energy of the parked PHEVs on the control market. Due to the fact that cars on average are parked 92% of the day, the availability of this capacity could be very high, even though it will be highly dependent on commuting patterns in peak hours. However, as each PHEV has a very small capacity from a grid perspective, it is necessary to implement an aggregating control system, managing a large number of vehicles. This paper presents strategies for an Aggregator to fulfill control bids on the German control markets. These strategies are tested with respect to reliability, efficiency and profitability in a Monte Carlo simulation model. The model is based on available data on the distributions of commuting departure times and travel distances, as well as average driving power consumption, PHEV battery capacities and the market constraints of the secondary control market in Germany.

Vehicle to grid — Reference architectures for the control markets in Sweden and Germany

Previous work has shown that it could be profitable on some control markets to use plug-in hybrid electric vehicles (PHEV) as control power resources. This concept, where battery driven vehicles such as PHEVs provide ancillary service to the grid is commonly referred to as Vehicle to Grid. The idea is to sell the capacity and energy of the parked PHEVs on the control market. Due to the fact that cars on average are parked 92% of the day, the availability of this capacity could be very high, even though it will be highly dependent on commuting patterns in peak hours. However, as each PHEV has a very small capacity from a grid perspective, it is necessary to implement an aggregating control system, managing a large number of vehicles. The present article investigates design requirements on such a system.

A Local Market Model for Urban Residential Microgrids with Distributed Energy Resources

Widespread adoption of onsite distributed generation providing households with their electricity supply could make them very significant actors in terms of their aggregated impact on the wider electric power system. This paper proposes an approach which brings community engagement to the case of distributed energy resource based electricity supply for urban residential buildings. Considering a future case for Stockholm, a community ownership and service-oriented setup is proposed which fosters the creation of a market for both traditional and new stakeholders in the electric power system while ensuring the integrity of control by individual apartments. Salient features include a hypothetical pricing model which encourages local trade of electricity between apartments guided by individual apartment behavior and decision making through bid strategies. A set of research themes devoted to understanding the trends in effect of changes in the integral concept components have been identified through simulations and subjected to initial analysis.

Modelling Framework and the Quantitative Analysis of Distributed Energy Resources in Future Distribution Networks

Abstract There has been a large body of statements claiming that the large-scale deployment of Distributed Energy Resources (DERs) could eventually reshape the future distribution grid operation in numerous ways. Thus, it is necessary to introduce a framework to measure to what extent the power system operation will be changed by various parameters of DERs. This article proposed a modelling framework for an overview analysis on the correlation between DERs. Furthermore, to validate the framework, the authors described the reference models of different categories of DERs with their unique characteristics, comprising distributed generation, active demand and electric vehicles. Subsequently, quantitative analysis was made on the basis of the current and envisioned DER deployment scenarios proposed for Sweden. Simulations are performed in two typical distribution network models for four seasons. The simulation results show that in general the DER deployment brings in the possibilities to reduce the power losses and voltage drops by compensating power from the local generation and optimizing the local load profiles.

Trustworthy Injection/Curtailment of DER in Distribution Network maintaining quality of Service

Future powers system is considering huge flux of information flow due to increase in Renewable Energy Sources. Due to the limited monitoring and management of SCADA systems, inclusion of DER at local distribution level is a challenging task for the Smart Grid. We discuss the non-functional requirement aspects and their implication in trustworthy systems. The paper also illustrates an engineering approach towards trustworthy ICT systems. We present a use case about Injection/Curtailment of DER in distributed network. Further, we argue the importance of Service Level Agreements as coordination tool for information exchange between DSO and DER for the provisioning of trustworthy services. Finally conclude that modeling of SLA using Multi Agent Systems is a viable approach towards Trustworthy future Smart Grid applications.

Domestic heat load aggregation strategies for wind following in electric distribution systems

This paper investigates the operation of a domestic heat load aggregator on the Swedish island of Gotland. In the considered business case the aim of the aggregator is to minimize the losses on the HVDC connection with the mainland matching local wind generation and load. A network model is implemented and static power flow simulations are performed to evaluate the aggregator profit and the effects of its actions on the network behavior. Various aggregator strategies are simulated in different wind penetration scenarios, several indices are calculated to compare the different cases on a quantitative base.

Stochastic evaluation of aggregator business models — Optimizing wind power integration in distribution networks

In order to limit the environmental impact of electricity production, renewable energy sources are expected to expand significantly. The current grid and production structure are not designed to absorb such quantities of intermittent power output and smart grids can provide promising solutions, like demand response. This paper presents the technical advantages of managing flexible demand through Aggregators in a centralized fashion. An optimization model is developed to evaluate the economic benefits induced by adapting instantaneous electricity consumption to renewable generation. The model presented can easily be adapted in a more general context, and tested for different scenarios. Further, a use case related to the Smart Grids project on the Swedish island of Gotland is simulated. The simulation results show that the Aggregator solution is technically feasible, but that the current market design is a barrier for a successful implementation.

A seasonal ARIMA model with exogenous variables for elspot electricity prices in Sweden

In a spot market, price prediction plays an indispensable role in maximizing the benefit of a producer as well as optimizing the utility of a consumer. This paper develops a seasonal ARIMA model with exogenous variables (SARIMAX) to predict day-ahead electricity prices in Elspot market, the largest day-ahead market for power trading in the world. Compared with the basic ARIMA model, SARIMAX has two distinct features: 1) A seasonal component is introduced to cope with weekly effect on price fluctuations. 2) Exogenous variables that exert influence on electricity prices are incorporated to make price predictions in the context of an integrated energy market. A detailed implementation of SARIMAX for Elspot market in Sweden is presented.

Simulating occupancy in office buildings with non-homogeneous Markov chains for Demand Response analysis

Demand Response (DR) is a promising solution to deal with supply/demand imbalances in the power systems. To appreciate the availability of DR, it is important to include end user behavior in the analysis. In this paper, a model that can generate representative occupancy profiles in single office rooms is presented. The used method is non-homogeneous Markov chain modeling, along with exploratory data analysis of occupancy data, and an estimation of occupancy levels for different months and weekdays. The Markov chain model is calibrated with occupancy sensor data from 24 single rooms collected from an office building floor in Sweden. The simulation results have been statistically compared with a test data set consisting of occupancy data in 23 other rooms on the same floor. It was shown that the model could reproduce key occupancy properties of the test data.

Assessment of Congestion Management Potential in Distribution Networks using Demand-Response and Battery Energy Storage

The integration of large shares of renewable energy sources in distribution grids runs the risk of outpacing the capacity of the network. Thus, high investment costs are expected at distribution system level to expand the existing grid to manage, among other challenges, anticipated congestion. This paper involves a study of the technical feasibility of using an ancillary service toolbox including day- and hour-ahead demand-response and battery energy storage as an alternative to grid expansion. The ancillary service toolbox is applied on radial distribution grids having large shares of renewable generation, controllable loads and power export capability to the overlaying power grid. The toolbox is simulated for a real use case presenting results on required demand-response participants and operation of flexibility resources for different congestion scenarios. The study concludes that the ancillary service solution is technically feasible for the use case, which may imply network investment deferral for distribution system operators.