Anders Thavlov

Application of Model Predictive Control for Active Load Management in a Distributed Power System With High Wind Penetration

Summary form only given. This paper introduces an experimental platform (SYSLAB) for the research on advanced control and power system communication in distributed power systems and one of its components-an intelligent office building (PowerFlexHouse), which is used to investigate the technical potential for active load management. It also presents in detail how to implement a thermal Model Predictive Controller (MPC) for the heaters power consumption prediction in the PowerFlexHouse. It demonstrates that this MPC strategy can realize load shifting, and using good predictions in MPC-based control, a better matching of demand and supply can be achieved. With this demand side control study, it is expected that MPC strategy for active load management can dramatically raise energy efficiency and improve grid reliability, when there is a high penetration of intermittent energy resources in the power system.

Model Predictive Controller for Active Demand Side Management with PV self-consumption in an intelligent building

This paper presents a Model Predictive Controller (MPC) for electrical heaters predictive power consumption including maximizing the use of local generation (e.g. solar power) in an intelligent building. The MPC is based on dynamic power price and weather forecast, considering users comfort settings to meet an optimization objective such as minimum cost and minimum reference temperature error. It demonstrates that this MPC strategy can realize load shifting, and maximize the PV self-consumption in the residential sector. With this demand side control study, it is expected that MPC strategy for Active Demand Side Management (ADSM) can dramatically save energy and improve grid reliability, when there is a high penetration of Renewable Energy Sources (RESs) in the power system.

Utilization of Flexible Demand in a Virtual Power Plant Set-Up

High penetration levels from renewable energy sources in large-scale power systems demand a high degree of flexibility in the transmission and distribution system. This paper presents a method for utilization of flexible demand in the low-voltage distribution system using the thermal mass of a building to defer power consumption from electric space heating. The power consumption for heating is controlled by an operational virtual power plant, which is sending a set point for requested power consumption to the building management system. An optimization problem is formulated such that the discrete dispatch of power from ten electric space heaters is following the power set point given constraints on the indoor comfort that is defined by the users of the building. The controlling method has been implemented in an intelligent office building and used for demonstration of flexible demand in the low voltage network.

Active load management in an intelligent building using model predictive control strategy

This paper introduces PowerFlexHouse, a research facility for exploring the technical potential of active load management in a distributed power system (SYSLAB) with a high penetration of renewable energy and presents in detail on how to implement a thermal model predictive controller for load shifting in PowerFlexHouse heaters power consumption scheme. With this demand side control study, it is expected that this method of demand response can dramatically raise energy efficiencies and improve grid reliability, when there is a high penetration of intermittent energy resources in the power system.

An aggregation model for households connected in the low-voltage grid using a VPP interface

To secure the stability in power systems with a high penetration from renewable energy sources, the demand side has to become more flexible than today. If the flexibility from the numerous units connected in the low-voltage grid is to be utilised, aggregation methods have to be developed. This paper presents an aggregation model using an interface defined by an operating virtual power plant. Simulations of the aggregator show that a large share of the power consumption, due to heating of households, can be postponed or accelerated in time to the benefit of the stability of the power system. Additionally the simulations show that peak demand due to heating can be significantly decreased.

Procedure for validation of aggregators providing demand response

As aggregators become viable sources of ancillary services, they will be required to undergo a validation process similar to the prequalification process of traditional generators. Aggregators are fundamentally different from traditional generators in that they are formed of a large quantity of small heterogeneous resources that are geographically distributed. Therefore, a new test procedure must be designed for the aggregator validation. This work proposes such a procedure and exemplifies is with a study case. The validation of aggregators is essential if aggregators are to be integrated succesfully into the power system.

Nonlinear economic model predictive control strategy for active smart buildings

Nowadays, the development of advanced and innovative intelligent control techniques for energy management in buildings is a key issue within the smart grid topic. A nonlinear economic model predictive control (EMPC) scheme, based on the branch-and-bound tree search used as optimization algorithm for solving the nonconvex optimization problem is proposed in this paper. A simulation using the nonlinear model-based controller to control the temperature levels of an intelligent office building (PowerFlexHouse) is addressed. Its performance is compared with a linear model-based controller. The nonlinear controller is shown very reliable keeping the comfort levels in the two considered seasons and shifting the load away from peak hours in order to achieve the desired flexible electricity consumption.

A Heat Dynamic Model for Intelligent Heating of Buildings

This article presents a heat dynamic model for prediction of the indoor temperature in an office building. The model has been used in several flexible load applications, where the indoor temperature is allowed to vary around a given reference to provide power system services by shifting the heating of the building in time. This way the thermal mass of the building can be used to absorb energy from renewable energy source when available and postpone heating in periods with lack of renewable energy generation. The model is used in a model predictive controller to ensure the residential comfort over a given prediction horizon.

A functional reference architecture for aggregators

Aggregators are considered to be a key enabling technology for harvesting power system services from distributed energy resources (DER). As a precondition for more widespread use of aggregators in power systems, methods for comparing and validating aggregator designs must be established. This paper proposes a functional reference architecture for aggregators to address this requirement.

Performance requirements modeling and assessment for active power ancillary services

New sources of ancillary services are expected in the power system. For large and conventional generation units the dynamic response is well understood and detailed individual measurement is feasible, which factors in to the straightforward performance requirements applied today. For secure power system operation, a reliable service delivery is required, yet it may not be appropriate to apply conventional performance requirements to new technologies and methods. The service performance requirements and assessment methods therefore need to be generalized and standardized in order to include future ancillary service sources. This paper develops a modeling method for ancillary services performance requirements, including performance and verification indices. The use of the modeling method and the indices is exemplified in two case studies.