Considering that most of the photovoltaic (PV) data are behind-the-meter (BTM), there is a great challenge to implement effective demand response projects and make a precise customer baseline (CBL) prediction. To solve the problem, this paper proposes a data-driven PV output power estimation approach using only net load data, temperature data, and solar irradiation data. We first obtain the relationship between delta actual load and delta temperature by calculating the delta net load from matching the net load of irradiation for an approximate day with the least squares method. Then we match and make a difference of the net load with similar electricity consumption behavior to establish the relationship between delta PV output power and delta irradiation. Finally, we get the PV output power and implement PV-load decoupling by modifying the relationship between delta PV and delta irradiation. The case studies verify the effectiveness of the approach and it provides an important reference to perform PV-load decoupling and CBL prediction in a residential distribution network with BTM PV systems.
Photovoltaic (PV) generation is increasing in distribution systems following policies and incentives to promote zero-carbon emission societies. Most residential PV systems are installed behind-the-meter (BTM). Due to single meter deployment that measures the net load only, this PV generation is invisible to distribution system operators causing a negative impact on the distribution system planning and local supply and demand balance. This paper proposes a novel data-driven BTM PV generation disaggregation method using only net load and weather data, without relying on other PV proxies and PV panels' physical models. Long Short-Term Memory (LSTM) is employed to build a generation difference fitted model (GDFM) and a consumption difference fitted model (CDFM) derived from weather data. Both difference fitted models are refined by a crossiteration with mutual output. Finally, considering the photoelectric conversion properties, the disaggregated generation results are acquired by the refined GDFM of changing input. The proposed method has been tested with actual smart meter data of Austin, Texas and proves to increase the disaggregated accuracy as compared to current state-of-the-art methods. The proposed method is also applicable to disaggregate BTM PV systems of different manufacturing processes and types.
Internet data centers are growing rapidly in recent years and they operate with intensive energy activity. Combined cooling, heating and power (CCHP) brings new opportunities for reducing the electricity cost in internet data centers. The main objective of this study is to optimize the energy resources scheduling in the data center coupled energy nets considering the involvement of CCHP and different demand response techniques. In this paper, internet data center coupled energy nets are proposed, where power grid, solar photovoltaic, CCHP, and battery energy storage systems are the primary energy sources. The adjunct residential buildings and commercial buildings near the internet data centers are also included in the proposed energy nets, where different types of load and demand response characteristics are utilized. A two-stage optimized energy management model considering the coordinated operation of CCHP and demand response technologies is established for internet data center coupled energy nets. In the day-ahead stage, the control objective is to minimize system cost while satisfying various constraints. Consider the electricity tariff chance between day-ahead market and real-time market, real-time control is implemented to minimize the imbalance cost between two electricity markets. Case studies are conducted on a practical internet data center coupled energy nets in Foshan City, China. It is observed that the proposed control framework can optimally schedule the energy resources in the energy network to meet system demand and improve the energy efficiency. The economic evaluation demonstrates that the proposed control scheme reduces system daily cost by 22.01%.INDEX TERMS Combined cooling, heating and power, data centers, mixed integer linear programming, renewable energy sources, two-stage optimal scheduling.
In the microgrid with high photovoltaic (PV) penetration, optimal sizing of battery energy storage system (BESS) has been a heated research topic in recent years. In the meanwhile, the high energy consumption of air-conditioned households is attracting more and more attention currently. In this paper, an optimal sizing method of BESS is developed for a smart microgrid with PV systems and air-conditioning resources. The proposed model is divided into two layers. In the first layer, the initial size of BESS is determined with consideration of photovoltaic output power and thermal buffering characteristics of air-conditioned households. In the second layer, the optimal size of BESS is proposed to minimize the system overall cost including BESS construction investment and microgrid system operation cost. The model is solved by differential evolutionary algorithm and iterative algorithms. Case studies demonstrate the effectiveness of the proposed method.
One of the key domains in smart cities is smart energy in which smart grid is a main focus. In recent years, with the development of smart grid, controllable air conditioning load participating in demand response projects and the application of renewable energy sources have drawn wide research interests. The integration of photovoltaic (PV) system and electric vehicles into the micro grid has also brought vitality to the stable operation of smart grids. In this paper, a novel control scheme is proposed to optimize the scheduling of building micro grid that integrate controllable air conditioner loads, PV panels and electric vehicles. The optimal operation problem is modeled and further converted into a mixed integer linear programming (MILP) problem whose objective function is minimizing the electricity cost of the building. The stochastic characteristics of electric vehicles are also considered in this paper to better model electric vehicle behaviors. Simulations are conducted on an office building micro grid and the simulation results verify the feasibility of proposed control strategy.
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