Dynamic demand response in smart buildings using an intelligent residential load management system

Arun, S. L.; Selvan, M. P.

doi:10.1049/iet-gtd.2016.1789

Cited by 44 publications

(27 citation statements)

References 22 publications

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“…Yang and Wang [23] address the trade-off between energy consumption and comfort level of smart building occupants using an innovative multi-objective evolutionary optimization algorithm. Arun and Selvan [24] propose a real-time load scheduling heuristic algorithm aimed at reducing the electricity bill and keep the total consumption under maximum demand limit considering the operating dynamics of a large number of controllable and non-controllable loads. Similarly, Lee et al [25] present a hidden Markov model for the scheduling of shiftable loads in households equipped with storage systems.…”

Section: Literature Reviewmentioning

confidence: 99%

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Energy scheduling of a smart microgrid with shared photovoltaic panels and storage: The case of the Ballen marina in Samsø

Carli

Dotoli

Jantzen

et al. 2020

Energy

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This paper focuses on the Model Predictive Control (MPC) based energy scheduling of a smart microgrid equipped with non-controllable (i.e., with fixed power profile) and controllable (i.e., with flexible and programmable operation) electrical appliances, as well as photovoltaic (PV) panels, and a battery energy storage system (BESS). The proposed control strategy aims at a simultaneous optimal planning of the controllable loads, the shared resources (i.e., the storage system charge/discharge and renewable energy usage), and the energy exchange with the grid. The control scheme relies on an iterative finite horizon on-line optimization, implementing a mixed integer linear programming energy scheduling algorithm to maximize the self-supply with solar energy and/or minimize the daily cost of energy bought from the grid under timevarying energy pricing. At each time step, the resulting optimization problem is solved providing the optimal operations of controllable loads, the optimal amount of energy to be bought/sold from/to the grid, and the optimal charging/discharging profile for the BESS. The proposed energy scheduling approach is applied to the demand side management control of the marina of Ballen, Samsø (Denmark), where a smart microgrid is currently being implemented as a demonstrator in the Horizon2020 European research project SMILE. Simulations considering the marina electric consumption (340 boat sockets, a service building equipped with a sauna and a wastewater pumping station, and the harbour master's office equipped with a heat pump), PV production (60kWp), and the BESS (237kWh capacity) based on a public real dataset are carried out on a one year time series with a 1 hour resolution. Simulations indicate that the proposed approach allows 90% exploitation of the production of the PV plant. Furthermore, results are compared to a naïve control approach. The MPC based energy scheduling improves the self-supply by 1.6% compared to the naïve control. Optimization of the business economy using the MPC approach, instead, yields to 8.2% savings in the yearly energy cost with respect to the naïve approach.

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Section: Literature Reviewmentioning

confidence: 99%

“…( )}, { * ( )}, % (0), ) (0), (0), (0) Procedure: 1 set t ← 0 2 iterate 3 get forecast data ( ), ( ), "#$ ( ), ! ( ), conditions % ( ), ) ( ), ( ), (t) 6solve the optimization problem (26) using(24) or (25) (Algorithm 2a or 2b) 7 apply only 0 ( + 1), % ( + 1), ) ( + 1), )! ( + 1), )* ( + 1), .!…”

mentioning

confidence: 99%

Energy scheduling of a smart microgrid with shared photovoltaic panels and storage: The case of the Ballen marina in Samsø

Carli

Dotoli

Jantzen

et al. 2020

Energy

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“…are net expected power of prosumer n, internal buying price and internal selling price, respectively, during the interval t, and the functions f 1 and f 2 are the equality and inequality constraints describing the limits on load operating intervals and prosumer satisfaction, respectively [22]. In this study, BGA is used to optimise the objective function.…”

Section: Prosumer Agentmentioning

confidence: 99%

An electric power trading framework for smart residential community in smart cities

Rao

Arun²,

Selvan

2019

IET Smart Cities

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This study proposes a multi-agent-based framework for Peer-to-Peer (P2P) power trading in a locality electricity market (LEM) for self-interested smart residential prosumers. In LEM, prosumers may sell (buy) their excess generation (demand) at a profitable market prices compared to utility prices to achieve a win-win outcome. In LEM, three agents namely locality electricity trading system (LETS), utility and prosumer act together to achieve P2P power trading in a day-ahead market. LETS computes the internal market prices employing any one of the market-clearing mechanisms and broadcasts it to the prosumers. Prosumers optimise the generation-demand schedule for the next day using residential energy management and trading system to achieve minimum electricity bill. The performance of the proposed framework is validated through different case studies on a residential locality with ten prosumers. The simulation is carried out using MATLAB parallel computation tool box and the load data is collected from the residential locality of National Institute of Technology Tiruchirappalli, India. It is evident from the simulation results that all the participants are economically benefited by P2P power trading. It is also found that the SDR mechanism in P2P outperforms and reduces the locality electricity bill by 27-68% under different operating conditions.

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“…Few of such approaches are by Nilotpal et al [6,11,12], wherein multiple AC scheduling problem has been modelled as a graph and solved using a fast greedy approach. Subsequently, a genetic algorithm-driven system for optimal load scheduling was proposed in [13][14][15], which describes their system's performance without and with local renewable generation, respectively. Li et al [16] have proposed an energy management system, which has dependency on solar PV power prediction.…”

Section: Related Literaturementioning

confidence: 99%

“…From the pseudocode, it can be inferred that algorithm calculates charging current using (13), where 98% of MDL has been kept to incorporate the minor fluctuations in real power…”

Section: Case Study 2: Ac With Dynamic Ev Charging (Second Control Almentioning

confidence: 99%

Agent‐based system to control the air‐conditioner and EV charging for residents in smart cities

2019

Self Cite

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Air-conditioner (AC) accounts for a significant share of residential energy consumption. Considering the widespread rise in electric vehicle (EV) usage, its charging would also contribute a considerable percentage of consumer's total energy consumption. Consequently, the concurrent operation of AC and EV charging would result in peaky load curves. Hence, this study proposes a system of agents for AC and EV charging applications, which incorporates load-management strategies to flatten the load curve. Thereby, the presented system includes two agents, namely: a smart load node for a thermostatically controlled load (SLN-TCL) and a smart battery charge controller. Subsequently, a subagent, namely micro-node, has been introduced to support SLN-TCL and to implement the concept of distributed temperature sensing (DTS). The implementation of DTS subdues the conventional temperature sensing mechanism of AC and ensures a more flexible operation. This study includes the design, development, and features of agents and subagents for AC and EV applications. Furthermore, this study also demonstrates the agent-based control actions for peak-shaving under real conditions to showcase the performance of this system.

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Dynamic demand response in smart buildings using an intelligent residential load management system

Cited by 44 publications

References 22 publications

Energy scheduling of a smart microgrid with shared photovoltaic panels and storage: The case of the Ballen marina in Samsø

Energy scheduling of a smart microgrid with shared photovoltaic panels and storage: The case of the Ballen marina in Samsø

An electric power trading framework for smart residential community in smart cities

Agent‐based system to control the air‐conditioner and EV charging for residents in smart cities

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