Novel incentive mechanism for end-users enrolled in DLC-based demand response programs within stochastic planning context

In this paper, a novel direct load control (DLC) planning based on providing free energy credits to residential end-users for their heating, ventilation, and air conditioning load during demand response (DR) events is proposed. The obtained credit can then be used by the end-users during relatively higher price periods free-of-cost to enable them lowering their energy procurement costs. Furthermore, the resulting reduction in the total household energy consumption considerably decreases the critical load demands in power systems, which is of vital importance for load-serving entities in maintaining the balance between supply and demand during peak load periods. In this regard, the aforementioned energy credits-based incentive mechanism is proposed for end-users enrolled in the DLC-based DR program, as a new contribution to the existing literature, testing it in a stochastic day-ahead planning context

Novel incentive mechanism for end-users enrolled in DLC-based demand response programs within stochastic planning context

\u3cp\u3eIn this paper, a novel direct load control (DLC) planning based on providing free energy credits to residential end-users for their heating, ventilation, and air conditioning load during demand response (DR) events is proposed. The obtained credit can then be used by the end-users during relatively higher price periods free-of-cost to enable them lowering their energy procurement costs. Furthermore, the resulting reduction in the total household energy consumption considerably decreases the critical load demands in power systems, which is of vital importance for load-serving entities in maintaining the balance between supply and demand during peak load periods. In this regard, the aforementioned energy credits-based incentive mechanism is proposed for end-users enrolled in the DLC-based DR program, as a new contribution to the existing literature, testing it in a stochastic day-ahead planning context.\u3c/p\u3

End-user comfort oriented day-ahead planning for responsive residential HVAC demand aggregation considering weather forecasts

abstract onl

Combining the flexibility from shared energy storage systems and DLC-based demand response of HVAC units for distribution system operation enhancement

In this study, a direct load control strategy for procuring flexibility from residential heating, ventilation, and air conditioning (HVAC) units and the optimal management of shared energy storage systems connected at different buses of a distribution system is proposed, as a new contribution with respect to earlier studies, aiming to minimize the energy demand during DR event periods. Moreover, an additional objective related to the minimization of the end-users' discomfort induced by the interruption of the HVAC units is considered, leading to the formulation of a bi-level optimization problem based on a second-order conic programming representation of the AC power flow equations. The effectiveness of the proposed methodology is demonstrated by performing simulations on a test system and comparisons with other approaches

An energy credit based incentive mechanism for the direct load control of residential HVAC systems incorporation in day-ahead planning

\u3cp\u3eThe increasing operational complexity of power systems considering the higher renewable energy penetration and changing load characteristics, together with the recent developments in the ICT field have led to more research and implementation efforts related to the activation of the demand side. In this manner, different direct load control (DLC) and indirect load control concepts have been developed and DLC strategies are considered as an effective tool for load serving entities (LSEs) with several real-world application examples. In this study, a new DLC strategy tailored for residential air-conditioners (ACs) participating in the day-ahead planning, based on offering energy credits to the enrolled end-users is proposed. The mentioned energy credits are then used by residential end-users to lower their energy procurement costs during peak-price periods. The strategy is formulated as a stochastic mixed-integer linear programming (MILP) model considering uncertainties related to weather conditions. The outcomes regarding the end-user comfort level and economic benefits are also analyzed.\u3c/p\u3

Combining the flexibility from shared energy storage systems and DLC-based demand response of HVAC units for distribution system operation enhancement

\u3cp\u3eIn this study, a direct load control strategy for procuring flexibility from residential heating, ventilation, and air conditioning (HVAC) units and the optimal management of shared energy storage systems connected at different buses of a distribution system is proposed, as a new contribution with respect to earlier studies, aiming to minimize the energy demand during DR event periods. Moreover, an additional objective related to the minimization of the end-users' discomfort induced by the interruption of the HVAC units is considered, leading to the formulation of a bi-level optimization problem based on a second-order conic programming representation of the AC power flow equations. The effectiveness of the proposed methodology is demonstrated by performing simulations on a test system and comparisons with other approaches.\u3c/p\u3

Assessment of demand-response-driven load pattern elasticity using a combined approach for smart households

\u3cp\u3eThe recent interest in the smart grid vision and the technological advancement in the communication and control infrastructure enable several smart applications at different levels of the power grid structure, while specific importance is given to the demand side. As a result, changes in load patterns due to demand response (DR) activities at end-user premises, such as smart households, constitute a vital point to take into account both in system planning and operation phases. In this study, the impact of price-based DR strategies on smart household load pattern variations is assessed. The household load datasets are acquired using model of a smart household performing optimal appliance scheduling considering an hourly varying price tariff scheme. Then, an approach based on artificial neural networks (ANN) and wavelet transform (WT) is employed for the forecasting of the response of residential loads to different price signals. From the literature perspective, the contribution of this study is the consideration of the DR effect on load pattern forecasting, being a useful tool for market participants such as aggregators in pool-based market structures, or for load serving entities to investigate potential change requirements in existing DR strategies, and effectively plan new ones.\u3c/p\u3

Demand response driven load pattern elasticity analysis for smart households

\u3cp\u3eThe recent interest in smart grid vision enables several smart applications in different parts of the power grid structure, where specific importance should be given to the demand side. As a result, changes in load patterns due to demand response (DR) activities at end-user premises, such as smart households, constitute a vital point to take into account both in system planning and operation phases. In this study, the assessment of the impacts of pricing based DR strategies on smart household load pattern variations is provided. The household load data sets are acquired from a provided model of a smart household, including appliance scheduling. Then, an artificial neural network (ANN) approach based on Wavelet Transform (WT) is employed for the forecasting of responsive residential load behaviors to different pricing schemes. From the literature perspective this study contributes by considering DR impacts on load pattern forecasting, being a very useful tool for market participants such as aggregators in future pool-based market structures, or for load serving entities to discuss potential change requirements in existing DR strategies, or even to effectively plan new ones.\u3c/p\u3

Comprehensive optimization model for sizing and siting of DG units, EV charging stations and energy storage systems

\u3cp\u3eThe sizing and siting of renewable resources-based distributed generation (DG) units has been a topic of growing interest, especially during the last decade due to the increasing interest in renewable energy systems and the possible impacts of their volatility on distribution system operation. This paper goes beyond the existing literature by presenting a comprehensive optimization model for the sizing and siting of different renewable resources-based DG units, electric vehicle charging stations, and energy storage systems within the distribution system. The proposed optimization model is formulated as a second order conic programming problem, considering also the time-varying nature of DG generation and load consumption, in contrast with the majority of the relevant studies that have been based on static values.\u3c/p\u3