In the energy system of the future, with high penetration of Renewable Energy Sources (RES), online, dispatch able power system support services will be required to efficiently integrate RES and to provide stable grid operation. Demand response is at present utilized as a bulk power system support service, which could be a promising distribution grid support mech anism with the appropriate specificity and controls. Demand response can potentially benefit system operation, expansion, and market efficiency while reducing the overall plant and capital cost investments [1]. Furthermore, demand response can be utilized to defer the need for power grid upgrades [1]. Demand response is provided by reducing or shifting elec tricity usage during peak periods in response to an external trigger and by following a predefined control strategy [2]. Recently, it has been demonstrated that this process can be automated and applied to conunercial buildings to provide demand response adapted to the ancillary services market [3]. Prosumers, proactive consumers that engage in demand re sponse, offer flexibility for certain appliances and local gener ation units. Flexibility is provided by trading off convenience in daily practices and comfort e.g., by relaxation of indoor climate metrics. Demand response provision requires an Infor mation and Communications Technology platform to provide management, aggregation, and scheduling of a large number of domestic appliances with flexible consumption [4], [2]. The success of a demand response strategy at present is limited by the lack of suitable tools for assessment of its potential and detailed understanding of causality. To circum vent this, simulation techniques may be used to evaluate a targeted demand response prior to any market control actions. Specifically, co-simulation, which offers the advantage of com bining domain-specific simulators, such as distribution grid planning models, and communications, enables the evaluation of demand response from a holistic and detailed perspective with a stronger foundation in building operations practices. In this paper, the co-simulation platform Virtual Grid Inte gration Laboratory (VirGIL) developed at Lawrence Berkeley National Laboratory is introduced and utilized to demonstrate its potential to assess demand response strategies [5]. VirGIL enables simple integration of new simulators and combines the state-of-the-art simulators in power systems, communications networks, modeling of buildings, and control engineering. Hereby, VirGIL aims at reducing the barriers of the industry to adopt new demand response programs. The main contribution of this paper is the coupling of a co-simulation environment with a Hardware-in-the-Loop (HiL) infrastructure for demand response assessment. Demand response is evaluated by using a ventilation system of a multi tentant residence integrated with the VirGIL co-simulation framework. The evaluation methods combine state-of-the-art tools for co-simulating power grid response with interactions of a running system of a building used as a living lab in Denmark [2]. To our knowledge, this is the first contribution to couple co-simulation with HiL to evaluate demand response in large residential buildings.
Reference REF ) introduced a co-simulation platform, called virtual integration laboratory (VirGIL) with HIL devices in order to evaluate DR programs in a residential building located in Denmark.
Hardware-in-the-Loop co-simulation of distribution Grid for demand response
2016 Power Systems Computation Conference (PSCC)
2016 Power Systems Computation Conference (PSCC)
Computer Science Engineering