Flattening the electricity consumption peak and reducing the electricity payment for residential consumers in the context of smart grid by means of shifting optimization algorithm

Oprea, Simona-Vasilica; Bârã, Adela; Ifrim, George

doi:10.1016/j.cie.2018.05.053

Cited by 49 publications

(22 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The problem in this work is that customers are considered at an aggregated level through demand price elasticity without the consideration of the different characteristics and flexibilities of customer end-uses [29]. In another past paper [30] the authors propose a shifting optimization algorithm for flattening electricity consumption in residential segments for the so-called advanced tariffs (ToU). Examples, like this last paper, are severely lacking in the physical basis of load models (for instance the demand of appliances must be moved all at once), customers which are very well-informed about their demand and can provide end-use load schedules to the aggregator, the load can change its patterns in different times without any problems (a furnace), or EES systems that run with an unknown state of charge.…”

Section: Literature Surveymentioning

confidence: 99%

“…Another aspect to be considered are the loss of customer service and the losses attributable to energy storage loads when the load service (hot water) and demand (electricity to heat conversion) occur with different time lags. This topic is not considered in lots of papers in the literature, mainly interested in optimization concerns of "static" loads which change demand irrespective of service or environmental variables which strongly condition demand, for instance in a past paper [30]. Table 3.…”

Section: Customer Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Integration of Demand Response and Photovoltaic Resources in Residential Segments

et al. 2018

View full text Add to dashboard Cite

The development of renewable sources in residential segments is basic to achieve a sustainable energy scenario in the horizon 2030–2050 because these segments explain around 25% of the final energy consumption. Demand Response and its effective coordination with renewable are additional concerns for residential segments. This paper deals with two problems: the demonstration of cost-effectiveness of renewables in three different scenarios, and the application of the flexibility of demand, performing as energy storage systems, to efficiently manage the generation of renewable sources while improving benefits and avoiding penalties for the customer. A residential customer in Spain has been used as example. The work combines the use of a commercial simulator to obtain photovoltaic generation, the monitoring of customer to obtain demand patterns, and the development of a Physically-Based Model to evaluate the capability of demand to follow self-generation. As a main result, the integration of models (load/generation), neglected in practice in other approaches in the literature, allows customers to improve revenue up to 20% and reach a basic but important knowledge on how they can modify the demand, development of new skills and, in this way, learn how to deal with the characteristics and limitations of both Demand and Generation when a customer becomes a prosumer. This synergy amongst demand and generation physically-based models boosts the possibilities of customers in electricity markets.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Section: Customer Descriptionmentioning

confidence: 99%

Integration of Demand Response and Photovoltaic Resources in Residential Segments

et al. 2018

View full text Add to dashboard Cite

show abstract

“…The concept of Virtual Budget as an efficient method for optimizing the electricity cost of demand scheduling using anticipation is proposed in [3], while a sliding window driven method, which utilizes streamed big data for real time electricity consumption optimal adjustment, is proposed and tested in [12]. Furthermore, an optimization algorithm for residential consumption pattern flattening by identifying the time-of-use tariff that minimizes the overall consumption cost is introduced in [13]. In [14], methods that assume the use of interruptible tasks are proposed, whereas in [15] an informatics solution that is based on the synergism of three models in optimizing household appliance management.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Multi-Objective Cost and Privacy Driven Load Morphing in Smart Electricity Grid Partition

Alamaniotis

Gatsis

2019

Energies

View full text Add to dashboard Cite

Utilization of digital connectivity tools is the driving force behind the transformation of the power distribution system into a smart grid. This paper places itself in the smart grid domain where consumers exploit digital connectivity to form partitions within the grid. Every partition, which is independent but connected to the grid, has a set of goals associated with the consumption of electric energy. In this work, we consider that each partition aims at morphing the initial anticipated partition consumption in order to concurrently minimize the cost of consumption and ensure the privacy of its consumers. These goals are formulated as two objectives functions, i.e., a single objective for each goal, and subsequently determining a multi-objective problem. The solution to the problem is sought via an evolutionary algorithm, and more specifically, the non-dominated sorting genetic algorithm-II (NSGA-II). NSGA-II is able to locate an optimal solution by utilizing the Pareto optimality theory. The proposed load morphing methodology is tested on a set of real-world smart meter data put together to comprise partitions of various numbers of consumers. Results demonstrate the efficiency of the proposed morphing methodology as a mechanism to attain low cost and privacy for the overall grid partition.

show abstract

mentioning

confidence: 99%

“…The load reduction or load profile flattening has also been done based on a shifting optimization algorithm. This method successfully lowers the electricity consumption and bill payment [18].A prominent challenge for a demand response program is the probabilistic nature of renewable sources. The conventional numerical methods to optimize the system often fail to reach the best possible solution.…”

mentioning

confidence: 99%

Object-Oriented Usability Indices for Multi-Objective Demand Side Management Using Teaching-Learning Based Optimization

Singh

Jha

2019

Energies

View full text Add to dashboard Cite

This paper proposes Object-Oriented Usability Indices (OOUI) for multi-objective Demand Side Management (DSM). These indices quantify the achievements of multi-objective DSM in a power network. DSM can be considered as a method adopted by utilities to shed some load during peak load hours. Usually, there are service contracts, and the curtailments or dimming of load are automatically done by service providers based on contract provisions. This paper formulates three indices, namely peak power shaving, renewable energy integration, and an overall usability index. The first two indices indicate the amount of peak load shaving and integration of renewable energy, while the third one combines the impact of both indices and quantifies the overall benefit achieved through DSM. The application of the proposed indices is presented through simulation performed in a grid-tied microgrid environment for a multi-objective DSM formulation. The adopted microgrid structure consists of three units of diesel generators and two renewable energy sources. Simulation has been done using MATLAB software. Teaching-Learning-Based Optimization (TLBO) is adopted as the optimization tool due to its simplicity and independency of algorithm-specific control parameters. Five different cases of renewable energy availability with results validate the efficiency of the proposed approach. The results indicate the usefulness in determining the suitable condition regarding DSM application.Energies 2019, 12, 370 2 of 25 problems is the main area of research for system operators. DSM has appeared as a potential solution for many of these problems.Any scheme that involves alteration in operation of a complex system should be reliable to a certain level. A reliability study of a power system with DSM is presented in [1]. Since a plan for load curtailment can only be prepared when load forecasting is reliable, stochastic optimization and Gray Wolf Optimization (GWO)-based load forecasting and subsequent DSM programs are presented in [2,3]. Load shifting is also an alternative method of performing DSM. In this aspect, a corrective load shifting program is found to be a better option to conventional preventive load shifting [4,5]. In another work, loads are categorized in rigid and flexible types, and DSM is performed keeping in mind the adaptability of the flexible loads [6]. A novel application of DSM to smoothing the peaks of load curve in a small demand area in the Sultanate of Oman is described in [7]. The key contribution was load profile prediction based upon which the DSM program was prepared. The DSM program developed here handles the issue of variable power generation from solar energy-based generation. In a work, the uncertainty due to wind power generation and its impact on the DSM scheme has been discussed [8]. The methodology discusses the fulfillment of two key objectives of DSM, i.e., minimization of emission and cost. Battery storage and diesel-based energy sources are also employed with solar or wind generators to improve the reliability ...

show abstract

Flattening the electricity consumption peak and reducing the electricity payment for residential consumers in the context of smart grid by means of shifting optimization algorithm

Cited by 49 publications

References 33 publications

Integration of Demand Response and Photovoltaic Resources in Residential Segments

Integration of Demand Response and Photovoltaic Resources in Residential Segments

Evolutionary Multi-Objective Cost and Privacy Driven Load Morphing in Smart Electricity Grid Partition

Object-Oriented Usability Indices for Multi-Objective Demand Side Management Using Teaching-Learning Based Optimization

Contact Info

Product

Resources

About