An MILP model for the optimal energy management of a smart household

Koltsaklis, Nikolaos E.; Panapakidis, Ioannis P.; Christoforidis, Georgios C.; Parisses, Constantinos

doi:10.1109/eem.2019.8916426

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…Since the output layer of DDPG is a tanh layer with output ranging from to , the output is first scaled up to to to meet constraint in Formula (7). Then it is bounded to meet the constraint in Formula (6)…”

Section: Q(s A|θ Q )mentioning

confidence: 99%

Deep Reinforcement Learning for Online Scheduling of Photovoltaic Systems with Battery Energy Storage Systems

Li,

Wu,

Pan

2024

Intell. and Converged Netw.

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A new online scheduling algorithm is proposed for photovoltaic (PV) systems with battery-assisted energy storage systems (BESS). The stochastic nature of renewable energy sources necessitates the employment of BESS to balance energy supplies and demands under uncertain weather conditions. The proposed online scheduling algorithm aims at minimizing the overall energy cost by performing actions such as load shifting and peak shaving through carefully scheduled BESS charging/discharging activities. The scheduling algorithm is developed by using deep deterministic policy gradient (DDPG), a deep reinforcement learning (DRL) algorithm that can deal with continuous state and action spaces. One of the main contributions of this work is a new DDPG reward function, which is designed based on the unique behaviors of energy systems. The new reward function can guide the scheduler to learn the appropriate behaviors of load shifting and peak shaving through a balanced process of exploration and exploitation.The new scheduling algorithm is tested through case studies using real world data, and the results indicate that it outperforms existing algorithms such as Deep Q-learning. The online algorithm can efficiently learn the behaviors of optimum non-casual off-line algorithms.

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Section: Q(s A|θ Q )mentioning

confidence: 99%

Deep Reinforcement Learning for Online Scheduling of Photovoltaic Systems with Battery Energy Storage Systems

Li,

Wu,

Pan

2024

Intell. and Converged Netw.

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“…In ref. [26], the model for smart household scheduling is presented by the authors. The approach bases the scheduling of all sorts of loads on optimal cost planning and mixed linear integer programming.…”

Section: Literature Reviewmentioning

confidence: 99%

Residential Prosumer Energy Management System with Renewable Integration Considering Multi-Energy Storage and Demand Response

Ali,

Aftab,

Akram

et al. 2024

Sustainability

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Rising energy demands, economic challenges, and the urgent need to address climate change have led to the emergence of a market wherein consumers can both purchase and sell electricity to the grid. This market leverages diverse energy sources and energy storage systems to achieve significant cost savings for consumers while providing critical grid support for utilities. In this study, an energy management system has been employed to tackle the optimization problem associated with various energy sources. This approach relies on mixed-integer linear programming (MILP) to optimize energy utilization while adhering to diverse constraints, yielding a feasible energy solution. This model is applied to real-world energy system consumption data and forecasts the most cost-effective day-ahead energy plans for different types of loads engaged in demand response. Furthermore, time-based charging and discharging strategies for electric vehicles and energy storage systems are considered, conducting a comprehensive analysis of energy costs across various storage devices. Our findings demonstrate that implementing this model can lead to an 18.26% reduction in operational costs when using lithium batteries and a remarkable 14.88% reduction with lead–acid batteries, particularly when integrating solar power and an EV into the system, while GHG is reduced by 36,018 grams/day for a load of 25 kW in one particular scenario. However, the analysis reveals that integrating wind power is not economically viable due to its comparatively higher operational costs.

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“…This work is based on the development of an optimization approach, namely, a MILP model, for optimal smart home energy management, including all types of smart loads (uncontrollable loads, curtailable, adjustable, uninterruptible and independent loads, uninterruptible and dependent loads, and thermostatic ones), RES, in the form of wind and photovoltaic power contribution, ESS, EV, and energy exchanges with the power grid. This work comprises an extension of the work presented in [25], on the grounds that it incorporates the following: i.…”

Section: Introductionmentioning

confidence: 99%

“…A peak load constraint has been added in the mathematical formulation, exerting significant influence on the decision making; iii. A more detailed temporal granularity has been employed, namely, 15 min time step instead of an hourly one in [25]; iv. The case studies executed include both summer and winter days, providing the ability to capture the seasonal fluctuation in terms of RES generation and the operation of thermostatic loads.…”

Section: Introductionmentioning

confidence: 99%

A Prosumer Model Based on Smart Home Energy Management and Forecasting Techniques

et al. 2021

Self Cite

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This work presents an optimization framework based on mixed-integer programming techniques for a smart home’s optimal energy management. In particular, through a cost-minimization objective function, the developed approach determines the optimal day-ahead energy scheduling of all load types that can be either inelastic or can take part in demand response programs and the charging/discharging programs of an electric vehicle and energy storage. The underlying energy system can also interact with the power grid, exchanging electricity through sales and purchases. The smart home’s energy system also incorporates renewable energy sources in the form of wind and solar power, which generate electrical energy that can be either directly consumed for the home’s requirements, directed to the batteries for charging needs (storage, electric vehicles), or sold back to the power grid for acquiring revenues. Three short-term forecasting processes are implemented for real-time prices, photovoltaics, and wind generation. The forecasting model is built on the hybrid combination of the K-medoids algorithm and Elman neural network. K-medoids performs clustering of the training set and is used for input selection. The forecasting is held via the neural network. The results indicate that different renewables’ availability highly influences the optimal demand allocation, renewables-based energy allocation, and the charging–discharging cycle of the energy storage and electric vehicle.

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An MILP model for the optimal energy management of a smart household

Cited by 5 publications

References 17 publications

Deep Reinforcement Learning for Online Scheduling of Photovoltaic Systems with Battery Energy Storage Systems

Deep Reinforcement Learning for Online Scheduling of Photovoltaic Systems with Battery Energy Storage Systems

Residential Prosumer Energy Management System with Renewable Integration Considering Multi-Energy Storage and Demand Response

A Prosumer Model Based on Smart Home Energy Management and Forecasting Techniques

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