Demand-Side Management of Domestic Electric Water Heaters Using Approximate Dynamic Programming

Al-jabery, Khalid K.; Xu, Zhezhao; Yu, Wenjian; Wunsch, Donald C.; Xiong, Jinjun; Shi, Yiyu

doi:10.1109/tcad.2016.2598563

Cited by 55 publications

(23 citation statements)

References 21 publications

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“…Demand side management (DSM) techniques have been investigated and proven to be viable alternatives to regulating the consumption of electricity from the consumer side with applications from residential to industrial sectors [1,34,27,29,8,2]. According to [46], the two techniques for DSM from the consumer side include energy efficiency improvement programs (insulation, sealing, solar water heating systems etc.)…”

Section: Introductionmentioning

confidence: 99%

Integration of demand side and supply side energy management resources for optimal scheduling of demand response loads – South Africa in focus

Monyei

Adewumi

2018

Electric Power Systems Research

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The energy crisis of 2008 in South Africa, due to electricity demand surpassing supply and a depleted electricity reserve margin has exposed the need for more synergy between home energy management systems (HEMS) and supply side energy management systems (SSEMS). Demand side management (DSM) techniques have been investigated and proven to be viable means of regulating electricity demand from the consumer side. However, the viabilty of DSM is dependent on the participation of willing consumers. In this paper, a combined energy management system (CEMS) is proposed to provide a platform for incorporating the demands and constraints of consumers (time of dispatch, reduction of electricity costs etc.) and suppliers (reduced operations cost, reduced emissions etc.). The proposed CEMS utilizes dynamic pricing (DP) and a standard deviation biased genetic algorithm (SDBGA) in minimizing the DSM window to be allocated to the DSM loads of consumers based on the multi-objective constraints. The Medupi power plant which has been modelled to utilize carbon capture and sequestration (CCS) technology is used in carrying out the dispatch of the participating DSM loads (cloth washers, cloth dryers and dish washers) for 100000 random residential customers. Results show that in dispatch option 1 (in which the user is in control of the start time), a lower cost of electricity of ZAR 373 218.40 is obtained compared to ZAR 416 280.20 by dispatch option 2 (in which the utility selects dispatch time for participating DSM loads) for the consumers. However, dispatch option 2 achieves a better minimized DSM window (14.94 MW), lower operating cost (about 1.6% lower than dispatch option 1), higher plant capacity utilization (87.92% efficiency) and a more evenly distributed profile. Keywords-demand side management, combined energy management system, home energy management system, supply side energy management system, standard deviation biased genetic algorithm Highlights Proposes a centralized energy management system for incorporating HEMS and SSEMS. Evaluates DSM for 100000 random homes having cloth washers, cloth dryers and dish washers. Uses a single DSM window to compare savings from dynamic pricing (DP) and time of use (TOU) pricing. Compares supply and consumer side benefits for leaving the control of DSM load start time selection with either the utility or the consumers.

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Section: Introductionmentioning

confidence: 99%

Integration of demand side and supply side energy management resources for optimal scheduling of demand response loads – South Africa in focus

Monyei

Adewumi

2018

Electric Power Systems Research

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“…Table 5 summarizes the major machine learning algorithms used in building control stage. MPC [96], [97], [98], [99] Kalman Filter [100], [101] Generic Algorithm [102] RL: value based [103], [104], [105], [106], [107], [108] RL: actor critic [109], [110] Learning building thermal dynamics for building control RC model and regression [97], [111], [112], [113] RC model and Generic Algorithm [114] Lighting control RL: value based [115] Window control RL: value based [116] Thermal Energy Storage control Non-linear programming [117] RL: value based [118] RL: actor critic [119], [120] Hot water control RL: value based [121] RL: actor critic [122] Comfort improvement…”

Section: Machine Learning For Building Controlmentioning

confidence: 99%

State-of-the-art on research and applications of machine learning in the building life cycle

Hong

Wang

Luo

et al. 2020

Energy and Buildings

250

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Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science.

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“…Generally, there are two types of DR programs being widely used while developing energy management programs; direct load control (DLC) and price based [14], [15]. In former, the utility has control to directly turn-off selected load during variation in frequency or overload conditions to maintain the power system stability [16]- [19]. Although, these schemes are useful in improving grid stability, however, this may lead to loss of social welfare and comfort of end users [20].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Pricing Mechanism With the Integration of Renewable Energy Source in Smart Grid

et al. 2020

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Day-ahead electricity pricing is an important strategy for electricity providers to improve grid stability through load scheduling. In this paper, we investigate a general framework for modelling electricity retail pricing based on load demand and market price information. Without any a priori knowledge, we have considered a finite time approach with dynamic system inputs. Our objective is to minimize the average system cost and rebound peaks through energy procurement price, load scheduling and renewable energy source (RES) integration. Initially, the energy consumption cost is calculated based on market clearing price and scheduled load. Then, through reformulation and subsequent modification of optimization problem, we utilize a day-ahead price information to construct individualized price profiles for each user, respectively. To analyse the applicability of proposed pricing policy, analytical solution is obtained which is further validated through comparison with solution obtained from genetic algorithm (GA). From results, it is observed that proposed price policy is non-discriminatory in nature and each user obtained a fair electricity tariff rather than a day-ahead price, which is based on load demand and consumption variation of other users. We also show that optimization problem is sequentially solved with bounded performance guarantee and asymptotic optimality. Finally, simulations are carried in different scenarios; aggregated load and market price, and aggregated load, individualized load, market price and proposed price. Results reveal that our proposed mechanism can charge the price to each user with 23.77% decrease or 5.12% increase based on system requirements. INDEX TERMS Demand response, optimization, non-discriminatory prices, individualized prices, smart grid, renewable energy. NOMENCLATURE

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Demand-Side Management of Domestic Electric Water Heaters Using Approximate Dynamic Programming

Cited by 55 publications

References 21 publications

Integration of demand side and supply side energy management resources for optimal scheduling of demand response loads – South Africa in focus

Integration of demand side and supply side energy management resources for optimal scheduling of demand response loads – South Africa in focus

State-of-the-art on research and applications of machine learning in the building life cycle

Dynamic Pricing Mechanism With the Integration of Renewable Energy Source in Smart Grid

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