Integrated energy management of a smart community with electric vehicle charging using scenario based stochastic model predictive control

Zhou, Fenglin; Li, Yaoyu; Wang, Wenyi; Pan, Chao

doi:10.1016/j.enbuild.2022.111916

Cited by 32 publications

(10 citation statements)

References 25 publications

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“…The research has focused on the economic outcome of the community and did not consider the self-sufficiency aspect of the community. The authors of [28] presented a community of residential customers that has the potential to be responsive. Also, the community's electric vehicles can be charged according to the transformer's operational constraints.…”

Section: B Literature Reviewmentioning

confidence: 99%

Increasing Self-Sufficiency of Energy Community by Common Thermal Energy Storage

2022

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Customers energy consumption pattern affects directly the grid burden, especially during peak hours. In recent years, many different control methods have been proposed to shift the energy consumption to off-peak hours through demand response (DR) management. In order to have effective DR energy management, optimization has a key role. Thus, increasing the benefits for the customers and encouraging them to consider the new controlling approaches in their daily energy consumption pattern is needed for increased customer participation. On the other hand, renewables are integrated with the buildings to decrease the buildings' energy costs and dependency on the grid utilities. This study moves a step further and considers a few numbers of neighboring houses as an energy community. The community commits to sharing their produced energy from the individual distributed solar system with each other and increasing their energy selfsufficiency by minimizing the import and export of power from/to the grid. This research focuses on applying common electric heat energy storage when community's own solar PV generation is used to thermal energy generation/storing in heat storage and compares it with the case in which each house has its own distributed thermal energy storage. Then, different sized thermal storages are tested for the community to find the best solution. The results are compared in terms of import and export of energy, annual costs and the payback-time. It is concluded that the community with common thermal energy storage could decrease the energy exchange with the grid and the payback-time of the investments could be reduced for the community members.INDEX TERMS Demand response, energy community, renewable energy resources, sustainable energy community, thermal energy storage

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Section: B Literature Reviewmentioning

confidence: 99%

Increasing Self-Sufficiency of Energy Community by Common Thermal Energy Storage

2022

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“…We find different stochastic optimization approaches within microgrids and (smart) energy communities in scientific literature. Energy management of a smart community with EV charging using a scenario-based stochastic model predictive control framework is presented in [52]. Among other stochastic parameters, moving-horizon probabilistic models are applied for the prediction of the arrival time of EVs.…”

Section: Stochastic Modeling and Optimization Of Energy Communitiesmentioning

confidence: 99%

A stochastic approach to dynamic participation in energy communities

Perger

Zwickl-Bernhard²,

Golab³

et al. 2022

Elektrotech. Inftech.

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With energy communities and local electricity markets on the rise, the possibilities for prosumers to be actively involved in the energy system increase, creating more complex settings for energy communities. This paper addresses the following research question: Does having knowledge about the future development in energy communities help make better decisions selecting new participants than without consideration of any future developments? Each year, the community is faced with the exit of existing members and a portfolio of possible new entrants with different characteristics. For this purpose, a bi-level optimization model for dynamic participation in local energy communities with peer-to-peer electricity trading, which is able to select the most suitable new entrants based on the preferences of the members of the original community, is extended to a stochastic dynamic program. The community wants to plan a few years ahead, which includes the following uncertainties: (i) which members leave after each period, and (ii) which are the potential new members willing to join the community. This paper’s contribution is a stochastic optimization approach to evaluate possible future developments and scenarios. The focus lies on the contractual design between the energy community and new entrants; the model calculates the duration of contracts endogenously. The results show a sample energy community’s decision-making process over a horizon of several years, comparing the stochastic approach with a simple deterministic alternative solution.

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“…Thus, it comes naturally to address propagating uncertainty and variability of RES by supporting real-time quantification of building thermal inertia and gas linepack. One promising method to reach this objective is the stochastic model predictive control (SMPC) strategy, which has numerous viable applications on the optimal control of IES [29]. SMPC is a control technique that uses a predictive model of a system and optimization algorithms to manage prediction errors more effectively and generate control actions that optimize a given objective over a finite time horizon [30].…”

Section: Introductionmentioning

confidence: 99%

A Stochastic MPC-Based Flexibility Scheduling Strategy for Community Integrated Energy System Considering Multi-Temporal-Spatial-Scale and Inertia Components

Zhang,

2024

Processes

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The network trend of isolated communities adds urgency to accelerate the deployment of community integrated energy systems (CIES). CIES effectively combines and optimizes multiple energy systems, leveraging their complementarity for efficient utilization and economical energy supply. However, the escalating intricacies of coupling multiple energy sources and the rising system uncertainties both pose challenges to flexibility scheduling of energy supply and demand. Additionally, the potential flexibility of building thermal inertia and pipeline gas linepack in diverse CIES, encompassing residential, commercial, and industrial communities, remains unexplored. To tackle these issues, a stochastic model predictive control (SMPC) based multi-temporal-spatial-scale flexibility scheduling strategy considering multiple uncertainty sources and system inertia components is proposed. First, the optimization model of CIES is formulated to improve operational flexibility and efficiency, resolve energy discrepancies and expand the capacity for renewable energy utilization. Then, the SMPC-based framework embedding an auto-regressive model and scenario generation method are established to make real-time corrections to the day-ahead scheduling stage and offset the prediction errors of uncertainty sources economically. Furthermore, thermal inertia of the aggregated buildings with different envelopes and linepack in gas pipelines are both leveraged to enhance the flexibility and synergy of CIES. Finally, a case study is executed to verify the effectiveness and applicability of the proposed strategy. The simulation results unequivocally demonstrate that this strategy successfully coordinates and harnesses complementary advantages from various energy sources, fostering a balanced energy supply-demand equilibrium across multiple temporal and spatial scales.

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Integrated energy management of a smart community with electric vehicle charging using scenario based stochastic model predictive control

Cited by 32 publications

References 25 publications

Increasing Self-Sufficiency of Energy Community by Common Thermal Energy Storage

Increasing Self-Sufficiency of Energy Community by Common Thermal Energy Storage

A stochastic approach to dynamic participation in energy communities

A Stochastic MPC-Based Flexibility Scheduling Strategy for Community Integrated Energy System Considering Multi-Temporal-Spatial-Scale and Inertia Components

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