Comparison of four rule-based demand response control algorithms in an electrically and heat pump-heated residential building

Alimohammadisagvand, Behrang; Jokisalo, Juha; Sirén, Kai

doi:10.1016/j.apenergy.2017.10.088

Cited by 76 publications

(42 citation statements)

References 36 publications

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“…The algorithms used an hourly DH price assuming that future price information is always available for 24 h forward. The trend of future hourly DH price was defined as rising, steady or falling by using the algorithm developed by Alimohammadisagvand et al [26]. It must be noted that hourly changing DH prices are not yet developed in the market, but they are expected [8].…”

Section: Modelling Of the Demand Response Strategies In Buildingsmentioning

confidence: 99%

“…As described earlier, revenues from electricity sales are considered in the heat production costs and the electricity prices used in the modelling represent the electricity prices in 2012 [31] and electricity tax and a dynamic transfer cost is added to it. The year 2012 was chosen because several recent studies on electric DR in Finland have also utilized that year, e.g., [26,32]. The dynamic transfer cost relates to the anticipation that the price of transmitting energy (c/kWh) varies according to the time of use [33].…”

Section: Simulated Dh Systemmentioning

confidence: 99%

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The Impact of Optimal Demand Response Control and Thermal Energy Storage on a District Heating System

et al. 2019

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Demand response has been studied in district heating connected buildings since the rollout of smart, communicating devices has made it cost-effective to control buildings’ energy consumption externally. This research investigates optimal demand response control strategies from the district heating operator perspective. Based on earlier simulations on the building level, different case algorithms were simulated on a typical district heating system. The results show that even in the best case, heat production costs can be decreased by only 0.7%. However, by implementing hot water thermal storage in the system, demand response can become more profitable, resulting in 1.4% cost savings. It is concluded that the hot water storage tank can balance district heating peak loads for longer periods of time, which enhances the ability to use demand response strategies on a larger share of the building stock.

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Section: Modelling Of the Demand Response Strategies In Buildingsmentioning

confidence: 99%

Section: Simulated Dh Systemmentioning

confidence: 99%

The Impact of Optimal Demand Response Control and Thermal Energy Storage on a District Heating System

et al. 2019

Self Cite

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“…The building sector contributes up to 30% of global annual GHG emissions through the use of fossil fuels and consumes up to 40% of all energy [74]. In making indoor thermal comfort in buildings [75][76][77][78][79][80] heating and cooling systems based on various technologies play the main role. Thus, the user's input in choosing a heating and cooling system should be analysed in order to control environmental impacts, energy saving and GW issues.…”

Section: Technology and Resourcesmentioning

confidence: 99%

Consumers’ Attitudes to Support Green Energy: A Case Study in Shanghai

et al. 2019

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Residents’ willingness to use green energy products is a major concern for different stakeholders and policymakers due to the reformed Chinese electricity market since 2015. This study focused on the Chinese consumer’s willingness to opt and pay for environmentally-friendly electricity sources in Shanghai’s residential sector. A questionnaire survey was used to find out the influence of gender, age, education, awareness and income level on consumers’ attitudes to alter their energy sources to green ones. The results indicated that income level and awareness are significant barriers in the usage of green energy products. Increasing the respondent’s awareness about the issues of non-green energy products convinced 97% of them to change their electricity sources completely or partly, in line with their monthly income. This clearly shows that clarifying the benefits of green energy products is a key tool to achieve a green environment in China.

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“…Sousa et al modeled the day-ahead DER optimization schedule algorithm with EV charging station constraints [20]. Furthermore, studies on the operation control of electric heating or heat storage systems in the building are intensively carried out to utilize thermal energy as DR resources [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Optimal Scheduling of Distributed Energy Resources in Residential Building under the Demand Response Commitment Contract

Baek

Kim

2019

Energies

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This study proposes optimal day-ahead demand response (DR) participation strategies and distributed energy resource (DER) management in a residential building under an individual DR contract with a grid-system operator. First, this study introduces a DER management system in the residential building for participation to the day-ahead DR market. The distributed photovoltaic generation system (PV) and energy-storage system (ESS) are applied to reduce the electricity demand in the building and sell surplus energy on the grid. Among loads in the building, lighting (LTG) and heating, ventilation, and air conditioning (HVAC) loads are included in the DR program. In addition, it is assumed that a power management system of an electric vehicle (EV) charging station is integrated the DER management system. In order to describe stochastic behavior of EV owners, the uncertainty of EV is formulated based on their arrival and departure scenarios. For measuring the economic efficiency of the proposed model, we compare it with the DER self-consuming operation model without DR participation. The problem is solved using mixed integer linear programming to minimize the operating cost. The results in summer and winter are analyzed to evaluate the proposed algorithm’s validity. From these results, the proposed model can be confirmed as reducing operation cost compared to the reference model through optimal day-ahead DR capacity bidding and implementation.

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Comparison of four rule-based demand response control algorithms in an electrically and heat pump-heated residential building

Abstract: These DRs are applied for heat generation system with two-storage tank system. Heat generation systems are a GSHP or storing electric heating system. The maximum savings in annual heating energy and cost are 10% and 15%.

Cited by 76 publications

References 36 publications

The Impact of Optimal Demand Response Control and Thermal Energy Storage on a District Heating System

The Impact of Optimal Demand Response Control and Thermal Energy Storage on a District Heating System

Consumers’ Attitudes to Support Green Energy: A Case Study in Shanghai

Optimal Scheduling of Distributed Energy Resources in Residential Building under the Demand Response Commitment Contract

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