Multi-Time Scale Optimal Scheduling of a Photovoltaic Energy Storage Building System Based on Model Predictive Control

Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a multi-time scale optimal scheduling strategy based on model predictive control(MPC)is proposed under the consideration of load optimization.First,load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature,and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation cost.Second,considering inter-day to intra-day source-load prediction error,an intraday rolling optimal scheduling strategy based on MPC is proposed that dynamically corrects the day-ahead dispatch results to stabilize system power fluctuations and promote photovoltaic consumption.Finally,taking an office building on a summer work day as an example,the effectiveness of the proposed scheduling strategy is verified.The results of the example show that the strategy reduces the total operating cost of the photovoltaic energy storage building system by 17.11%,improves the carbon emission reduction by 7.99%,and the photovoltaic consumption rate reaches 98.57%,improving the system’s low-carbon and economic performance.

Microstructure and Characterization of Capric-stearic Acid/Modified Expanded Vermiculite Thermal Storage Composites

In order to improve the thermal storage capacity of expanded vermiculite（EV） based formstable composite PCM（FS-PCM） via organic modification of EV, first, EV was modified with a sodium stearate（Na St） as surface modifier, and organic EV（OEV） with hydrophobicity and higher adsorption capacity for fatty acid was obtained. A novel capric-stearic acid eutectic（CA-SA）/OEV FS-PCM with high thermal storage capacity was then developed. OEV and CA-SA/OEV were characterized by scanning electron microscopy（SEM）, X-ray diffraction（XRD）, Fourier transform infrared spectroscopy（FTIR）, differential scanning calorimetry（DSC）, thermal gravimetry（TG）, and thermal cycling test. Results showed that OEV has obvious hydrophobicity and a higher adsorption capacity for fatty acid. Its adsorption ratio has increased by 48.71% compared with that of EV. CA-SA/OEV possesses high thermal storage density（112.52 J/g）, suitable melting temperature（20.49 ℃）, good chemical compatibility, excellent thermal stability and reliability, indicating great application potential for building energy efficiency. Moreover, organic modification of inorganic matrix may offer novel options for improving its adsorption capacity for organic PCMs and increasing heat storage capacity of corresponding FS-PCMs.

Evaluation of model predictive control(MPC)of solar thermal heating system with thermal energy storage for buildings with highly variable occupancy levels

The presence or absence of occupants in a building has a direct effect on its energy use,as it influences the operation of various building energy systems.Buildings with high occupancy variability,such as universities,where fluctuations occur throughout the day and across the year,can pose challenges in developing control strategies that aim to balance comfort and energy efficiency.This situation becomes even more complex when such buildings are integrated with renewable energy technologies,due to the inherently intermittent nature of these energy source.To promote widespread integration of renewable energy sources in such buildings,the adoption of advanced control strategies such as model predictive control(MPC)is imperative.However,the variable nature of occupancy patterns must be considered in its design.In response to this,the present study evaluates a price responsive MPC strategy for a solar thermal heating system integrated with thermal energy storage(TES)for buildings with high occupancy variability.The coupled system supplies the building heating through a low temperature underfloor heating system.A case study University building in Nottingham,UK was employed for evaluating the feasibility of the proposed heating system controlled by MPC strategy.The MPC controller aims to optimize the solar heating system’s operation by dynamically adjusting to forecasted weather,occupancy,and solar availability,balancing indoor comfort with energy efficiency.By effectively integrating with thermal energy storage,it maximizes solar energy utilization,reducing reliance on non-renewable sources and ultimately lowering energy costs.The developed model has undergone verification and validation process,utilizing both numerical simulations and experimental data.The result shows that the solar hot water system provided 63%heating energy in total for the case study classroom and saved more than half of the electricity cost compared with that of the original building heating system.The electricity cost saving has been confirmed resulting from the energy shifting from high price periods to medium to low price periods through both active and passive heating energy storages.

Soft Open Point Planning in Renewable-dominated Distribution Grids with Building Thermal Storage

With an increasing integration of intermittent distributed energy resources(DERs),the consequent voltage excursion and thermal overloading issues limit the self-sufficiency of distribution networks(DNs).The concept of soft open point(SOP)has been proposed as a promising solution to improve the hosting capacity of DNs.In this paper,considering the ability of building thermal storage(BTS)to increase the penetration of renewable energy in DNs,we provide an optimal planning framework for SOP and DER.The optimal planning model is aimed at minimizing the investment and operational costs while respecting various constraints,including the self-sufficiency requirement of the DN,SOP,building thermal storage capacity and DER operations,etc.A steady-state SOP model is formulated and linearized to be incorporated into the planning framework.To make full use of the BTS flexibility provided by ubiquitous buildings,a differential equation model for building thermal dynamics is formulated.A hybrid stochastic/robust optimization approach is adopted to depict the uncertainties in renewable energy and market prices.IEEE 33-bus feeder and a realistic DN in the metropolitan area of Caracas are tested to validate the effectiveness of the proposed framework and method.Case studies show that SOP/BTS plays a complementary and coordinated coupling role in the thermo-electric system,thereby effectively improving the hosting capacity and self-sufficiency of DNs.