Tackling the Load Uncertainty Challenges for Energy Consumption Scheduling in Smart Grid

Samadi, Pedram; Mohsenian‐Rad, Hamed; Wong, Vincent W. S.; Schober, Robert

doi:10.1109/tsg.2012.2234769

Cited by 159 publications

(120 citation statements)

References 31 publications

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“…Similar stochastic control algorithms using forecasts in buildings [27] and micro-grids [28] can also be found in the literature showing how forecasts and a priori network data can successfully be incorporated into optimal stochastic control algorithms. Samadi et al take into account load uncertainty and study demand response scheduling using variable pricing tariffs with successful results [29]. Recently, Murillo-Sanchez et al created a stochastic optimization framework for the operation of an electrical network by a DNO [30].…”

Section: Introductionmentioning

confidence: 99%

The Real-Time Optimisation of DNO Owned Storage Devices on the LV Network for Peak Reduction

et al. 2014

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Energy storage is a potential alternative to conventional network reinforcement of the low voltage (LV) distribution network to ensure the grid's infrastructure remains within its operating constraints. This paper presents a study on the control of such storage devices, owned by distribution network operators. A deterministic model predictive control (MPC) controller and a stochastic receding horizon controller (SRHC) are presented, where the objective is to achieve the greatest peak reduction in demand, for a given storage device specification, taking into account the high level of uncertainty in the prediction of LV demand. The algorithms presented in this paper are compared to a standard set-point controller and bench marked against a control algorithm with a perfect forecast. A specific case study, using storage on the LV network, is presented, and the results of each algorithm are compared. A comprehensive analysis is then carried out simulating a large number of LV networks of varying numbers of households. The results show that the performance of each algorithm is dependent on the number of aggregated households. However, on a typical aggregation, the novel SRHC algorithm presented in this paper is shown to outperform each of the comparable storage control techniques.

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Section: Introductionmentioning

confidence: 99%

The Real-Time Optimisation of DNO Owned Storage Devices on the LV Network for Peak Reduction

et al. 2014

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“…For instance, a day ahead pricing scheme has been used in [14] for appliance scheduling in order to minimize total energy cost. Authors in [15] proposed a Linear Programming (LP) model to optimize the home energy consumption and total cost.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced System Architecture for Optimized Demand Side Management in Smart Grid

et al. 2016

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Demand Side Management (DSM) through optimization of home energy consumption in the smart grid environment is now one of the well-known research areas. Appliance scheduling has been done through many different algorithms to reduce peak load and, consequently, the Peak to Average Ratio (PAR). This paper presents a Comprehensive Home Energy Management Architecture (CHEMA) with integration of multiple appliance scheduling options and enhanced load categorization in a smart grid environment. The CHEMA model consists of six layers and has been modeled in Simulink with an embedded MATLAB code. A single Knapsack optimization technique is used for scheduling and four different cases of cost reduction are modeled at the second layer of CHEMA. Fault identification and electricity theft control have also been added in CHEMA. Furthermore, carbon footprint calculations have been incorporated in order to make the users aware of environmental concerns. Simulation results prove the effectiveness of the proposed model.

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“…Firstly, new control strategies may include more controllable loads. A hybrid control model [69,70] may be popular in practical applications, including the main power system, renewable sources, and many different kinds of controllable loads. The aggregator model [71], bi-level, and multilayer control models will be studied in detail.…”

Section: Trend Development Of Controllable Load Approachesmentioning

confidence: 99%

Controllable Load Management Approaches in Smart Grids

Shen

Jiang

2015

Energies

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Abstract:With rapid smart grid technology development, the customer can actively participate in demand-side management (DSM) with the mutual information communication between the distributor operation company and the smart devices in real-time. Controllable load management not only has the advantage of peak shaving, load balance, frequency regulation, and voltage stability, but is also effective at providing fast balancing services to the renewable energy grid in the distributed power system. The load management faces an enormous challenge as the customer has a large number of both small residential loads and dispersed renewable sources. In this paper, various controllable load management approaches are discussed. The traditional controllable load approaches such as the end users' controllable appliances, storage battery, Vehicle-to-Grid (V2G), and heat storage are reviewed. The "broad controllable loads" management, such as the microgrid, Virtual Power Plant (VPP), and the load aggregator are also presented. Furthermore, the load characteristics, control strategies, and control effectiveness are analyzed.

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Tackling the Load Uncertainty Challenges for Energy Consumption Scheduling in Smart Grid

Cited by 159 publications

References 31 publications

The Real-Time Optimisation of DNO Owned Storage Devices on the LV Network for Peak Reduction

The Real-Time Optimisation of DNO Owned Storage Devices on the LV Network for Peak Reduction

An Enhanced System Architecture for Optimized Demand Side Management in Smart Grid

Controllable Load Management Approaches in Smart Grids

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