Modeling and Optimizing Energy Supply and Demand in Home Area Power Network (HAPN)

Minhas, Daud Mustafa; Frey, Georg

doi:10.1109/access.2019.2962660

Cited by 10 publications

(13 citation statements)

References 52 publications

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“…The proposed scheme scheduled the smart home appliances; however, the integration of the photovoltaic panel and the use of mixedinteger linear programming increases the complexity and cost of the deployment. Similar approaches for smart home appliances scheduling is proposed in [16,17]. The proposed approach also adopted the flexibility of MILP to schedule the smart home appliances and the energy demand is fulfilled with the help of installing a PV system.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Scheduling of Operational Time for Smart Home Appliances Based on Reinforcement Learning

2020

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The scheduling of the operational time of household appliances requires several parameters to be tuned according to the available energy supplied to a smart home. However, scheduling of operational time of multiple appliances in a smart home itself is the NP-hard problem and thus requires an intelligent, heuristic method to be solved in polynomial time. In this research work, we propose Real-time Scheduling of Operational Time of Household Appliances based on the well-known value iterative reinforcement learning called Quality learning (RSOTHA-QL). The proposed RSOTHA-QL scheme operates in two phases. In the first phase, the agents of the Q learning act by interacting with the smart home environment and obtain a reward. The reward value is further utilized to schedule the operational time of household appliances in the next state ensuring minimum energy consumption. In the second phase, the dissatisfaction arises due to scheduling of operational time of the household appliances of the home user is maintained by categorizing the household appliances into three groups: 1) deferrable, 2) non-deferrable, and 3) controllable. Besides, using the shared memory synchronization phenomenon, the agents attached to each appliance of the smart home become coordinated. The simulation and experiments are performed in a smart home scenario comprised of a single user and multiple appliances. As compared with our previous research work using the Least Slack Time (LST) scheduling algorithm and scheduling based on demand-response strategy, it is revealed that the operational time of the household appliances is efficiently scheduled to reduce the energy consumption and dissatisfaction level of the home users significantly.

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Section: Related Workmentioning

confidence: 99%

Real-Time Scheduling of Operational Time for Smart Home Appliances Based on Reinforcement Learning

2020

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“…These loads can be washing machine, dishwashing machine, and clothes dryer, etc. [8]. The model of shiftable loads can be expressed as:…”

Section: ) Shiftable Loadsmentioning

confidence: 99%

“…That is, HEMSs are in-line with the smart grid paradigm shift. In [8], the structure of smart homes was illustrated, which includes HEMS, energy storage devices and solar array. To implement IDR, combined cooling, heating, and power (CCHP) systems can make use of the complementary characteristics of multi-energy and have significant energy supply effects [9].…”

Section: Introductionmentioning

confidence: 99%

Pricing Strategy of Multi-Energy Provider Considering Integrated Demand Response

Yang

Liu

2020

IEEE Access

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Residential users (RUs) are the vital component of terminal energy consumption. The development and application of integrated energy system (IES) and smart homes has promoted RUs to actively take part in the trading with multi-energy provider (MEP) for its preferential energy prices and services. This paper proposes a pricing strategy of MEP by using a Stackelberg game-based bi-level programming model. In the upper level model, the adjustment coefficient of electric power price is optimized by MEP to increase the trading probability with RUs. In the lower level model, an integrated demand response (IDR) program is proposed for RUs to optimize the flexible loads in home energy management system (HEMS). Specially, a HEMS is composed of a smart interactive terminal, a micro combined cooling, heating, and power (mCCHP) system and multi-energy loads. Case study shows that, on one hand, the energy optimization based on IDR can help RUs manage their multi-energy loads and reduce the expected energy cost. On the other hand, the proposed price strategy of MEP can increase the trading probability, which promote more RUs to trade with MEP, thus increasing the MEP's benefit by 12.29%. The research proves that the proposed strategy is a win-win strategy and it is efficient in the pre-decisionmaking progress for MEP in the energy trading market.INDEX TERMS Integrated demand response (IDR), residential user (RU), multi-energy provider (MEP), Stackelberg game, pricing strategy, bi-level programing

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“…N ANO-GRID (NG) is a significant building block of a smart grid. It integrates various energy supply entities (ESEs), energy storage devices (ESDs), and intelligent energy devices (IEDs) within a single power network [1], [2]. Recently, a hybrid ac/dc NG configuration is introduced, that takes the advantage of both ac and dc configurations along with off-grid mode support [3], [4].…”

Section: Introductionmentioning

confidence: 99%

“…Whereas, the centralized secondary control mechanism (e.g., energy scheduler) is in charge of performing various optimization operations. It optimizes the energy flow, performing load demand and solar energy forecasting, economic dispatch, and power scheduling [2]. This control phenomenon usually predicts energy prices, power demands, and determines the amount of power that can be exchanged among various interconnected ESEs.…”

Section: Introductionmentioning

confidence: 99%

Rolling Horizon Based Time-Triggered Distributed Control for AC/DC Home Area Power Network

Minhas

Frey

2021

IEEE Trans. on Ind. Applicat.

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An energy-efficient smart home generally integrates renewable energy sources (RESs) with intelligent energy devices (IEDs) to make itself cost-efficient. However, the stochastic nature of the RESs and IEDs makes the power network uncertain, and it degrades the economic efficiency of the smart home. Hence a co-simulation based intelligent home energy management system (HEMS) is proposed in this article. It interconnects the rolling horizon-based model predictive energy scheduling mechanism with robust control strategies. It manages the load following energy supply entities (ESEs) optimally and efficiently. A time-ahead scheduler integrates an optimal algorithm to estimate the cost-optimal (e.g., minimizing total energy cost) decision signals for various ESEs by incorporating component based losses and efficiencies. Besides, a real-time distributed robust control strategy is established executing a coordinated energy sharing mechanism by incorporating an auxiliary power source. Time-triggered low-jitter wireless communication architecture is adopted to transfer the optimal decision signals from the central scheduler to the distributed device level local controller. A scenario based comparison of various ac/dc models of a home area power network evaluates the strength of the prospective HEMS.

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Modeling and Optimizing Energy Supply and Demand in Home Area Power Network (HAPN)

Cited by 10 publications

References 52 publications

Real-Time Scheduling of Operational Time for Smart Home Appliances Based on Reinforcement Learning

Real-Time Scheduling of Operational Time for Smart Home Appliances Based on Reinforcement Learning

Pricing Strategy of Multi-Energy Provider Considering Integrated Demand Response

Rolling Horizon Based Time-Triggered Distributed Control for AC/DC Home Area Power Network

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