Dispatch of direct load control using dynamic programming

Hsu, Yuan‐Yih; Su, Chih-Chiang

doi:10.1109/59.119246

Cited by 160 publications

(6 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Denote by l t i the cooling load of apartment i at time t (in kW), and denote by p the price at which every kWh is charged. In order to encourage consumers to use less energy for cooling, which constitutes a significant amount of the domestic load in warm-climate countries [6,7], the electricity supplier offers a discount scheme. Specifically, each apartment in a block is offered a binary option of signing up to the scheme or not.…”

Section: Coordinating Cooling Loads Discount Schemementioning

confidence: 99%

“…First, we explain the workings of iOPT, the pseudocode of which is presented in Algorithm 1. Using a heuristic, iOPT searches for a set of cooling actions that satisfy the constraints of an individual apartment as per Eq (7). This heuristic, which we call the discomfort of an apartment, represents the discrepancy between an apartment's preferences (as outlined in section user comfort model) and the temperature profile resulting from a cooling plan found by the algorithm.…”

Section: Computationally Efficient Optimisation Of Loadsmentioning

confidence: 99%

See 1 more Smart Citation

The Shapley value for a fair division of group discounts for coordinating cooling loads

Maleki

Rahwan²,

Ghosh

et al. 2020

PLoS ONE

View full text Add to dashboard Cite

We consider a demand response program in which a block of apartments receive a discount from their electricity supplier if they ensure that their aggregate load from air conditioning does not exceed a predetermined threshold. The goal of the participants is to obtain the discount, while ensuring that their individual temperature preferences are also satisfied. As such, the apartments need to collectively optimise their use of air conditioning so as to satisfy these constraints and minimise their costs. Given an optimal cooling profile that secures the discount, the problem that the apartments face then is to divide the total discounted cost in a fair way. To achieve this, we take a coalitional game approach and propose the use of the Shapley value from cooperative game theory, which is the normative payoff division mechanism that offers a unique set of desirable fairness properties. However, applying the Shapley value in this setting presents a novel computational challenge. This is because its calculation requires, as input, the cost of every subset of apartments, which means solving an exponential number of collective optimisations, each of which is a computationally intensive problem. To address this, we propose solving the optimisation problem of each subset suboptimally, to allow for acceptable solutions that require less computation. We show that, due to the linearity property of the Shapley value, if suboptimal costs are used rather than optimal ones, the division of the discount will be fair in the following sense: each apartment is fairly "rewarded" for its contribution to the optimal cost and, at the same time, is fairly "penalised" for its contribution to the discrepancy between the suboptimal and the optimal costs. Importantly, this is achieved without requiring the optimal solutions.

show abstract

Section: Coordinating Cooling Loads Discount Schemementioning

confidence: 99%

Section: Computationally Efficient Optimisation Of Loadsmentioning

confidence: 99%

The Shapley value for a fair division of group discounts for coordinating cooling loads

Maleki

Rahwan²,

Ghosh

et al. 2020

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Hsu et al developed a DPbased optimization strategy to reduce the system's energy-producing costs for the DLC dispatch. As a consequence, the dispatch DLC approaches and the unit commitment issue were integrated, and a DP strategy was developed to address both issues (Hsu and Su 1991).…”

Section: Stochastic Approachmentioning

confidence: 99%

A comprehensive overview on demand side energy management towards smart grids: challenges, solutions, and future direction

et al. 2023

View full text Add to dashboard Cite

Demand-side management, a new development in smart grid technology, has enabled communication between energy suppliers and consumers. Demand side energy management (DSM) reduces the cost of energy acquisition and the associated penalties by continuously monitoring energy use and managing appliance schedules. Demand response (DR), distributed energy resources (DER), and energy efficiency (EE) are three categories of DSM activities that are growing in popularity as a result of technological advancements in smart grids. During the last century, the energy demand has grown significantly in tandem with the increase in the global population. This is related to the expansion of business, industry, agriculture, and the increasing use of electric vehicles. Because of the sharp increase in global energy consumption, it is currently extremely difficult to manage problems such as the characterization of home appliances, integration of intermittent renewable energy sources, load categorization, various constraints, dynamic pricing, and consumer categorization. To address these issues, it is critical to examine demand-side management (DSM), which has the potential to be a practical solution in all energy demand sectors, including residential, commercial, industrial, and agricultural. This paper has provided a detailed analysis of the different challenges associated with DSM, including technical, economic, and regulatory challenges, and has proposed a range of potential solutions to overcome these challenges. The PRISMA reviewing methodology is adopted based on relevant literature to focus on the issues identified as barriers to improving DSM functioning. The optimization techniques used in the literature to address the problem of energy management were discussed, and the hybrid techniques have shown a better performance due to their faster convergence speed. Gaps in future research and prospective paths have been briefly discussed to provide a comprehensive understanding of the current DSM implementation and the potential benefits it can offer for an energy management system. This comprehensive review of DSM will assist all researchers in this field in improving energy management strategies and reducing the effects of system uncertainties, variances, and restrictions.

show abstract

“…By managing demand, DLC helps in maintaining the stability of the electric grid, preventing outages, and reducing the need for additional power plants. DLC is increasingly integrated with smart grid technology, allowing for more efficient and responsive control of energy use [17]. By reducing peak demand, DLC can decrease the need for utilities to rely on less efficient and more polluting power plants, thus contributing to environmental conservation.…”

Section: Introductionmentioning

confidence: 99%

PV-OPTIM: A Software Architecture and Functionalities for Prosumers

Bâra,

Oprea

2023

Electronics

View full text Add to dashboard Cite

The future development of the energy sector is influenced by Renewable Energy Sources (RES) and their integration. The main hindrance with RES is that their output is highly volatile and less predictable. However, the utility of the RES can be further enhanced by prediction, optimization, and control algorithms. The scope of this paper is to disseminate a smart Adaptive Optimization and Control (AOC) software for prosumers, namely PV-OPTIM, that is developed to maximize the consumption from local Photovoltaic (PV) systems and, if the solar energy is not available, to minimize the cost by finding the best operational time slots. Furthermore, PV-OPTIM aims to increase the Self-Sustainable Ratio (SSR). If storage is available, PV-OPTIM is designed to protect the battery lifetime. AOC software consists of three algorithms: (i) PV Forecast algorithm (PVFA), (ii) Day Ahead Optimization Algorithm (DAOA), and (iii) Real Time Control Algorithm (RTCA). Both software architecture and functionalities, including interactions, are depicted to promote and replicate its usage. The economic impact is related to cost reduction and energy independence reflected by the SSR. The electricity costs are reduced after optimization and further significantly decrease in case of real-time control, the percentage depending on the flexibility of the appliances and the configuration parameters of the RTCA. By optimizing and controlling the load, prosumers increase their SSR to at least 70% in the case of small PV systems with less than 4 kW and to more than 85% in the case of PV systems over 5 kW. By promoting free software applications to enhance RES integration, we estimate that pro-environmental attitude will increase. Moreover, the PV-OPTIM provides support for trading activities on the Local Electricity Markets (LEM) by providing the deficit and surplus quantities for the next day, allowing prosumers to set-up their bids.

show abstract

Dispatch of direct load control using dynamic programming

Cited by 160 publications

References 16 publications

The Shapley value for a fair division of group discounts for coordinating cooling loads

The Shapley value for a fair division of group discounts for coordinating cooling loads

A comprehensive overview on demand side energy management towards smart grids: challenges, solutions, and future direction

PV-OPTIM: A Software Architecture and Functionalities for Prosumers

Contact Info

Product

Resources

About