Minimization of building energy cost by optimally managing PV and battery energy storage systems

Bonthu, Rama Kishore; Ha, Pham Ngoc; Aguilera, Ricardo P.; Ha, Q.P.

doi:10.1109/icems.2017.8056442

Cited by 18 publications

(5 citation statements)

References 13 publications

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“…Demand response (DR) and distributed storage (DS) have been implemented in [25] through agent-based EMS. A feed-in tariff (FIT) and time of use (TOU) pricing-based strategy is proposed in [26] to get relief from peak charges. A multi-agent control system has been proposed in [27] for optimally exchanging information among multi-microgrid in the energy market system paradigm.…”

Section: Related Work and Motivationmentioning

confidence: 99%

Multi-Micro Grid System Reinforcement Across Deregulated Markets, Energy Resources Scheduling and Demand Side Management Using a Multi-Agent-Based Optimization in Smart Grid Paradigm

Sajid,

Altamimi,

Kazmi

et al. 2024

IEEE Access

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Multi-agent based small scaled smart grid reinforcement scheme is proposed to manage energy resources by enhancing resilience to supply power to critical loads in peak demand by leveraging demand side management (DSM) for smoothing load profile and optimal energy storage system (ESS) scheduling in response to grid cost. Interconnected microgrids comprise diverse energy resources that allow sharing energy to balance fluctuation by deploying a market that rewards cheap energy. Three stages are defined in this proposed scheme. In the first stage, without DER, consumption charges are reduced to 7.1% through DSM, but customer comfort is compromised. In the second stage, microgrids comprise RER; energy consumptionrelated charges are reduced to 92.2% by the feed-in tariff (FIT) Program. In the final stage, the inclusion of energy market for trading among microgrids (ESS, diesel and RER) resulting in reduced charges greater than charges incurred from the grid is defined by 122.1%. The findings show that the average power drawn from utility grid increased from 134.7kW to 205.3kW, and peak demand decreased from 591kW to 495kW. Integer linear programming optimization technique is applied to allow the optimal dispatch of microgrids generation to reduce the operation cost to 42.64%. Less reliance on the grid, flexible energy use, and avoiding costly electricity purchases are achieved through DSM, effective resources scheduling, and energy trading.INDEX TERMS Inter-connected multi-microgrids, multi-agent system, demand response program, market system, optimized scheduling, reverse power flow, real-time energy pricing

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Section: Related Work and Motivationmentioning

confidence: 99%

Multi-Micro Grid System Reinforcement Across Deregulated Markets, Energy Resources Scheduling and Demand Side Management Using a Multi-Agent-Based Optimization in Smart Grid Paradigm

Sajid,

Altamimi,

Kazmi

et al. 2024

IEEE Access

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“…Genetic algorithms have also been used to optimize the building thermal loads [29,30]. Reduced energy costs and improved occupant thermal comfort within buildings were achieved by using particle swarm optimization [31,32]. Dynamic programming was employed by Benjamin Heymann and Jiménez-Estévez [33] to reduce the energy costs while meeting the building load requirements.…”

Section: Literature Review and Contributionmentioning

confidence: 99%

Solar+ Optimizer: A Model Predictive Control Optimization Platform for Grid Responsive Building Microgrids

et al. 2020

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With the falling costs of solar arrays and battery storage and reduced reliability of the grid due to natural disasters, small-scale local generation and storage resources are beginning to proliferate. However, very few software options exist for integrated control of building loads, batteries and other distributed energy resources. The available software solutions on the market can force customers to adopt one particular ecosystem of products, thus limiting consumer choice, and are often incapable of operating independently of the grid during blackouts. In this paper, we present the “Solar+ Optimizer” (SPO), a control platform that provides demand flexibility, resiliency and reduced utility bills, built using open-source software. SPO employs Model Predictive Control (MPC) to produce real time optimal control strategies for the building loads and the distributed energy resources on site. SPO is designed to be vendor-agnostic, protocol-independent and resilient to loss of wide-area network connectivity. The software was evaluated in a real convenience store in northern California with on-site solar generation, battery storage and control of HVAC and commercial refrigeration loads. Preliminary tests showed price responsiveness of the building and cost savings of more than 10% in energy costs alone.

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“…To minimise

L_{f}

, a particle swarm optimisation algorithm [36] is used owing to its simplicity and robustness. Initially, random values of d are assigned to particles of the swarm.…”

Section: Iot‐based Dependable Controlmentioning

confidence: 99%

IoT‐enabled dependable control for solar energy harvesting in smart buildings

Phung

2019

IET Smart Cities

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Efficiency and reliability have been essential requirements for energy generation in smart cities. This study presents the design and development of dependable control schemes for microgrid management, which can be seamlessly integrated into the management system of smart buildings. Here, to recover from failures in the solar energy system of a building microgrid, dependable controllers are proposed along with their hardware implementation. The system features the use of Internet of Things (IoT) as its core to coordinate the operation of multiple subsystems in a scalable manner. The control scheme uses a number of controllers cooperatively functioning via a token-based mechanism within the network to provide redundancy and thus reliability in solar tracking. The system exploits data from not only local in-situ sensors but also online sources via IoT networks for fault-tolerant control. Experiments conducted in a 12-storey building indicate that the harvested solar energy meets the design requirement while the control reliability is maintained in face of communication or hardware disruptions. The results confirmed the validity of the proposed approach and its applicability to energy management in smart buildings.

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Minimization of building energy cost by optimally managing PV and battery energy storage systems

Cited by 18 publications

References 13 publications

Multi-Micro Grid System Reinforcement Across Deregulated Markets, Energy Resources Scheduling and Demand Side Management Using a Multi-Agent-Based Optimization in Smart Grid Paradigm

Multi-Micro Grid System Reinforcement Across Deregulated Markets, Energy Resources Scheduling and Demand Side Management Using a Multi-Agent-Based Optimization in Smart Grid Paradigm

Solar+ Optimizer: A Model Predictive Control Optimization Platform for Grid Responsive Building Microgrids

IoT‐enabled dependable control for solar energy harvesting in smart buildings

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