Improving the benefits of demand response participation in facilities with distributed energy resources

Roldán-Blay, Carlos; Escrivá-Escrivá, Guillermo; Roldán-Porta, Carlos

doi:10.1016/j.energy.2018.12.102

Cited by 34 publications

(19 citation statements)

References 28 publications

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“…The energy demand of a city is variable at different times during the day with trends remaining polarized. The peak energy demand required by cities becomes increasingly larger with social development and economic growth [1]. In order to meet the growing peak energy demand, the traditional approach is to increase the configuration capacity of the energy production system and transmission system continuously through methods such as using a standby diesel generator and upgrading the transmission system [2].…”

Section: State Of the Artmentioning

confidence: 99%

“…The time-of-use electricity price uses a static price scheme based on a predefined selling price, which usually maintains the same daily electricity price throughout the week, the month, the season, or the entire year [32]. The advantages of time-of-use pricing scheme include the following: (1) it is easy to follow; (2) it has a stable daily participating ratio; and (3) it can effectively shift the peak loads to valley loads [33]. Additionally, from Section 2, as the load and electricity cost price are divided into a peak segment and valley segment, a two-stage time-of-use electricity price is adopted to determine the electricity selling price of the PIES.…”

Section: Price Pattern and Demand Functionmentioning

confidence: 99%

“…The TR and new load peaks under 12 scenarios of load profiles are illustrated in Table 1. When the DR occurs, i.e., TR 0, the cost price of cooling, heating, and electricity should be recalculated by using the C-P sub-model and Algorithm (1) according to the load profile at this time. The TR and new load peaks under 12 scenarios of load profiles are illustrated in Table 1.…”

Section: Essential Information and Preliminary Calculationmentioning

confidence: 99%

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Study on Pricing Mechanism of Cooling, Heating, and Electricity Considering Demand Response in the Stage of Park Integrated Energy System Planning

Liu

2020

Applied Sciences

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With the opening of the Chinese electricity market, as a retailer that provides energy services to consumers, the park-integrated energy system (PIES) not only serves as an effective way to earn benefits and reduce carbon emissions but also impacts the energy consumption characteristics of consumers. The PIES implements this function by adjusting the energy selling price in free energy markets. The pricing mechanism model (P-M model) is established to obtain the energy selling price in the planning and design stages. In this model, the impact of the demand response on the energy configuration and the impact of the changes in energy configuration on the energy cost price are both considered. Additionally, the optimal result ensures that both the consumers and the PIES benefit simultaneously. The reactive demand response zone, which represents a consumer trap, is found in numerical studies. The results indicate the following: (1) from the perspective of P-M model optimization, the benefit exclusive point of the PIES is the optimal solution in the short term; (2) from the perspective of the long-term benefit, the ultimate result in the relationship between the PIES and consumers is that the PIES will share its profits with consumers; in other words, benefit sharing point is the optimal solution for the long term.

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Section: State Of the Artmentioning

confidence: 99%

Section: Price Pattern and Demand Functionmentioning

confidence: 99%

Section: Essential Information and Preliminary Calculationmentioning

confidence: 99%

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Study on Pricing Mechanism of Cooling, Heating, and Electricity Considering Demand Response in the Stage of Park Integrated Energy System Planning

Liu

2020

Applied Sciences

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“…As long as the battery does not reach its maximum state of charge (SOC), the PV energy surpluses will be used to charge the battery. Another option to improve self-sufficiency of PV systems is shifting the consumption of deferrable loads by demand-side management to periods with PV-surpluses [15,16]. The creation of cooperative microgrids with different SAPV generators has also been proposed [17,18] to address this problem.…”

Section: Introductionmentioning

confidence: 99%

Accurate Sizing of Residential Stand-Alone Photovoltaic Systems Considering System Reliability

et al. 2020

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In rural areas or in isolated communities in developing countries it is increasingly common to install micro-renewable sources, such as photovoltaic (PV) systems, by residential consumers without access to the utility distribution network. The reliability of the supply provided by these stand-alone generators is a key issue when designing the PV system. The proper system sizing for a minimum level of reliability avoids unacceptable continuity of supply (undersized system) and unnecessary costs (oversized system). This paper presents a method for the accurate sizing of stand-alone photovoltaic (SAPV) residential generation systems for a pre-established reliability level. The proposed method is based on the application of a sequential random Monte Carlo simulation to the system model. Uncertainties of solar radiation, energy demand, and component failures are simultaneously considered. The results of the case study facilitate the sizing of the main energy elements (solar panels and battery) depending on the required level of reliability, taking into account the uncertainties that affect this type of facility. The analysis carried out demonstrates that deterministic designs of SAPV systems based on average demand and radiation values or the average number of consecutive cloudy days can lead to inadequate levels of continuity of supply.

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“…Regarding demand response (DR) programs, the potential for participation in facilities is significantly increased by the distributed energy resources and especially the energy storage systems. With the participation in DR programs, the roles of the consumers change from a passive entity to an active entity that manages both local consumption and generation resources [4]. DR constitutes a modification of load profile in response to monetary or price signals, and thus provides flexibility conditioning, fridges, washing machine) and active (i.e., DG, ESS, vehicle-to-grid, PV) loads [18].…”

Section: Introductionmentioning

confidence: 99%

Demand Response Optimization Using Particle Swarm Algorithm Considering Optimum Battery Energy Storage Schedule in a Residential House

et al. 2019

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Demand response as a distributed resource has proved its significant potential for power systems. It is capable of providing flexibility that, in some cases, can be an advantage to suppress the unpredictability of distributed generation. The ability for participating in demand response programs for small or medium facilities has been limited; with the new policy regulations this limitation might be overstated. The prosumers are a new entity that is considered both as producers and consumers of electricity, which can provide excess production to the grid. Moreover, the decision-making in facilities with different generation resources, energy storage systems, and demand flexibility becomes more complex according to the number of considered variables. This paper proposes a demand response optimization methodology for application in a generic residential house. In this model, the users are able to perform actions of demand response in their facilities without any contracts with demand response service providers. The model considers the facilities that have the required devices to carry out the demand response actions. The photovoltaic generation, the available storage capacity, and the flexibility of the loads are used as the resources to find the optimal scheduling of minimal operating costs. The presented results are obtained using a particle swarm optimization and compared with a deterministic resolution in order to prove the performance of the model. The results show that the use of demand response can reduce the operational daily cost.

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Improving the benefits of demand response participation in facilities with distributed energy resources

Cited by 34 publications

References 28 publications

Study on Pricing Mechanism of Cooling, Heating, and Electricity Considering Demand Response in the Stage of Park Integrated Energy System Planning

Study on Pricing Mechanism of Cooling, Heating, and Electricity Considering Demand Response in the Stage of Park Integrated Energy System Planning

Accurate Sizing of Residential Stand-Alone Photovoltaic Systems Considering System Reliability

Demand Response Optimization Using Particle Swarm Algorithm Considering Optimum Battery Energy Storage Schedule in a Residential House

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