Fifth German-Austrian IBPSA Conference RWTH Aachen University CO-SIMULATION OF A SMART HOME MODEL BASED ON A MICRO ELECTRICITY MARKET W. El-Baz1, and P. Tzscheutschler2 1,2Institute of Energy Economics and Application Technology, Technical University of Munich, Munich, Germany [email protected], [email protected] ABSTRACT Torreglosa, Fernández, & Jurado, 2013) a fuzzy logic based EMS has been presented, while in (Gudi, Wang, Demand Side Management (DSM) represents one of & Devabhaktuni, 2012; Pollhammer & Kupzog, 2011) the most important factors in increasing the overall heuristic optimization approaches were used to electrical system efficiency. To achieve an automated optimizes the DSM operation. Others have handled DSM, an Energy Management System (EMS) must be the problem as a linear programing problem such as applied within the building. Throughout this (Kang, Park, Oh, Noh, & Park, 2014). Also, genetic contribution, an EMS based on a micro electricity algorithms techniques have been applied as presented market model, in which all the devices interact based in (Jayasekara & Wolfs, 2011). Furthermore, some on the energy costs to minimize the overall energy researchers were interested in basing their decision costs, will be presented. The EMS calculates the cost based on next two hours or day-ahead forecast for the signal and distributes it over to the supply and demand electricity prices, user’s electrical load or both as side devices so that they can take their own sale or presented in (Leon-garcia, 2010). Also, other purchase decisions. The simulation is based on co- approaches were interested in associating the simulation between SimulationX and Simulink. prediction system with a real-time continuous Keywords: Simulink-Modelica Co-Simulation, User optimization algorithm to make up for the forecast behavior, Decentralized Energy Management System, mismatching or unforeseeable events (Barbato & Micro Market, Micro Cogeneration Carpentieri, 2012; Di Giorgio & Liberati, 2014). INTRODUCTION To sum up, the presented approaches within the literature are interested in a centralized system, where Financial incentives plays an important role in all the devices are controlled by EMS. Then, the EMS encouraging the users to shift their loads according to gathers up all the available requests for scheduling and the grid prices or available energy resources, yet the feeds back an operational signal to the device. Such shifting decision needs a continuous load monitoring. approaches have always assumed an absolute control Such thing represents a significant burden over the over the future smart devices and perfect system user. Also, it would be challenging to optimize the compatibility. In reality, every household appliances load shifting manually. Energy Management Systems manufactures would like to have its own controller (EMS) represent one of the most common solutions that is fully aware of system capabilities so that a for Demand Side Management (DSM) automation pleasant customer experience can be guaranteed. within the residence. The main motive behind the Thus, integrating several devices from different EMS isn’t only to handle the common household manufactures might represent a challenge to the EMS. appliance loads within the house, but also to manage The objective of this contribution is to present a co- the upcoming Electric Vehicles (EVs) or Hybrid simulation between an electricity prediction system Electrical Vehicles (HEVs) loads which will represent and a decentralized micro-market based EMS. The a significant addition on the user’s residence and on whole simulation works on exchanging data between the national grid as well. SimulationX, which represents the home model in Throughout the literature, several EMS approaches addition to the EMS, and Simulink, which provides a have been presented that works towards several goals. day-ahead prediction and continuous prediction Yet, most of them was targeting peak load clipping, correction to the EMS. The modeled EMS is based on minimizing energy costs, minimizing CO2 emissions, the micro market approach. It works on providing the maximizing use of the energy supplies within the household devices a cost signal that is formulated house (Di Giorgio & Pimpinella, 2012; Ikeda et al., based on the electricity demand forecast, electricity 2011; Stluka, Godbole, & Samad, 2011; Warren, stock market price, available in-house energy sources 2014). The approaches used to reach these goals have such as Photovoltaic (PV) and micro Combined Heat varied along with the system installation, the house and Power cycle (CHP). Furthermore, different available devices, and its configuration. In (García, configuration and in-house energy sources capacities - 30 - Fifth German-Austrian IBPSA Conference RWTH Aachen University were tested to show out the optimum system and consequently, they might have a common request configuration that could be applied within a Smart and ask to operate at the same hour, cheapest hour. In Home for the applied operation scenarios. this case, the micro market controller gives the priority to the highest load, then the other devices DECENTRALIZED EMS automatically reschedule their request depending on Overview on the Micro Market Approach the new cost signal provided. Several household devices and micro CHP control Demand Side Devices strategies presented in the literature are designed to All the household appliances can be considered operate according to the real-time prices (RTP) demand side devices as they are all energy consuming (Houwing, 2011; Nyeng & Ostergaard, 2011), but the devices, yet only few of them have the ability to shift RTP presented by the grid aren’t representative their own loads. Within this simulation the freezer, enough to the operating loads inside the house, or the washing machine, dishwasher, tumble dryer and EV in-house energy sources use. The micro market were considered to have the ability to shift their load. adapted approach creates a market among both of the Other household appliance were disconnected from supply side devices (i.e., PV and micro CHP), demand the electricity market, yet their electrical load is side devices (i.e., household appliances) and storages. considered within the grid. Through this market, every supply or demand device For the dishwasher, washing machine, and the tumble can have its own controller which depends on the RTP dryer, the same controller was used. The function of signal proposed by the grid, yet this signal will be this controller is simply to find the minimum cost spot substituted by the micro market signal. and reserve it to its own device. Those devices were The micro market is designed to receive the RTP, considered uninterruptable, in another words, they along with purchase and sale requests from all kind of cannot reserve two different spaced market spots, but devices and storages, then it formulates the cost signal rather consecutive ones. Yet, the electrical vehicle and transmits it back to all the devices, so that every controller have the luxury to reserve multiple spaced device can take its own decision. The cost calculation market spots, as batteries charging can be stopped at function depends basically on equation 1 below, any moment and then resumed at a later time. where D represents the electricity demand, S The freezer controller has been using different represents the electricity supply, and C represents the strategy, since the freezer should be working costs. continuously. Also, the freezer has the advantage of (퐷 −푠 −푠 −푠 )×퐶 + 푅푒푠푖푑푒푛푡 푃푉 퐶퐻푃 퐵푎푡푡푒푟푖푒푠 퐺푟푖푑 thermal storage. The controller of the freezer is 퐶푃푉+퐶퐶퐻푃+퐶퐵푎푡푡푒푟푖푒푠 퐶퐴푣푒푟푎푒 = (1) 퐷푅푒푠푖푑푒푛푡 continuously working on monitoring the market It should be mentioned that all the variables average cost of each hour. Whenever it recognizes an represented in equation 1 are vectors representing increase in the average cost of the upcoming hour, it either the average cost of the next 24 or 48 hours, increases the cooling temperature so that it increases depending on the associated prediction systems. the switching off period of the compressor in the following hour. Micro Market Controller Supply Side Devices The micro market controller has been coded in Modelica and could be divided into two basic Throughout the modeling process, two main types of functions, the cost modifier and the load modifier supply side devices were considered in the analysis, function. The main purpose behind using those two which are the PV and the micro CHPs. Integrating PV functions is to prevent oscillations. These two function into the system was simply through sharing a work co-operatively to blind the device out of predefined PV power output prognosis with the micro recognizing the cost increase due to its own purchase market EMS. Since the PV output is dependent only so that it proceeds with the same purchase order rather on the solar irradiation, PV wouldn’t be interacting than moving to another slot. Figure 8 represents the with the market but rather submitting information high-level topology of the micro market along with all about its feed-in tariff and available capacities for each the devices connection. hour of the day. The micro market doesn’t have any optimization On the contrary of the PV, the micro CHP can function, it is just responsible for the average cost modulate itself depending on the market price, or the signal formulation and transmission to the devices. As user’s need. Since the micro CHP co-generates heat soon as the market receive a purchase request from the and electricity, a heat storage had to be associated. The device, it increases the market cost of this hour and main function of the heat storage is to decouple the transmits it back to all the other devices. If this cost heat generation from the electricity. Consequently, it got relatively higher, the other devices automatically will provide the micro CHP with a space to interact choose different operation hours. Yet, it could happen with the micro market cost signal. that a sudden market cost changes due to In this simulation model, the micro CHP controller unforeseeable user behavior or a false prediction.