Kangping Li

Kangping Li
Shanghai Jiao Tong University | SJTU

Ph.D. in Electrical Engineering

About

78
Publications
6,678
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1,733
Citations

Publications

Publications (78)
Article
Full-text available
The technological advancement in the communication and control infrastructure helps those smart households (SHs) more actively participate in the incentive-based demand response (IBDR) programs. As the agent facilitating the SHs' participation in the IBDR program, load aggregators (LAs) need to comprehend the available SHs' demand response (DR) cap...
Article
This paper addresses the optimal decision problem of a distributed energy resources (DER) aggregator who manages wind turbines, solar PV systems and battery energy storage (BES) units while implementing real-time pricing (RTP) demand response program. The DER aggregator can procure electricity by bidding in the electricity market and scheduling its...
Article
Full-text available
Accurate aggregated baseline load (ABL) estimation is critical for the implementation of incentive-based demand response (DR). The increasing penetration of invisible behind-the-meter photovoltaic (BTM-PV) systems makes the net load less predictable, thus posing a significant challenge to ABL estimation. Current direct estimation approaches will pr...
Article
Accurate aggregated baseline load (ABL) estimation is very important for the demand response (DR) compensation settlement between system operators and DR aggregators. Current ABL estimation methods totally ignore the spatial correlation between load patterns of different customers, which will lead to large errors when the load pattern on the DR eve...
Article
In the last decade, a number of severe urban power outages have been caused by extreme natural disasters, e.g., hurricanes, snowstorms and earthquakes, which highlights the need for rethinking current planning principles of urban energy systems and expanding the classical reliability-oriented view. In addition to being reliable to low-impact and hi...
Article
Full-text available
Buildings’ achievable energy flexibility refers to the real load reduction amount in an incentive-based demand response (DR) event, which presents dynamic, subjective, and uncertain characteristics. It is different from the buildings’ theoretical energy flexibility, which refers to its physical load reduction potential during a certain time period...
Article
Accurate household profiles (e.g., house type, number of occupants) identification is the key to the successful implementation of behavioral demand response. Currently, supervised learning methods are widely adopted to identify household profiles using smart meter data. Such methods could achieve promising performance in the case of sufficient labe...
Article
The aggregator, an emerging entity in the electricity market, gathers and creates market power for the small flexible resources, which traditionally contains distributed generation, electric storage and flexible load. Recently, the exploding growth of IT demand gives rise to the data centers, whose participation in demand response (DR) is becoming...
Article
With the wide deployment of charging piles and the development of V2G (vehicle-to-grid) technology, electric vehicles (EVs) will have more opportunities to participate in the operation and scheduling of electric power system through the EV aggregator (EVA), an intermediate agent between the power grid and EV users. Quantitatively evaluating the res...
Article
Residential customers account for an indispensable part in the demand response (DR) program for their capability to provide flexibility when the system required. However, their available DR capacity has not been fully comprehended by the aggregator, who needs the information to bid accurately on behalf of the residential customers in the market tra...
Article
The precise minute time scale forecasting of an individual PV power station output relies on accurate prediction of cloud distribution, which can lead to dramatic fluctuation of PV power generation. Precise cloud distribution information is mainly achieved by ground-based total sky imager, then the future cloud distribution can also be achieved by...
Article
Accurate monthly electricity consumption forecasting (ECF) can help retailers enhance the profitability in deregulated electricity markets. Most current methods use monthly load data to perform monthly ECF, which usually produces large errors due to insufficient training samples. A few methods try to use fine-grained smart-meter data (e.g., hourly...
Article
Distributed energy resources (DER), especially wind and photovoltaic power, and demand response (DR) are highly valued in recent years for their advantages on environmental protection, sustainable development, and so on. However, their volatility poses double risks to the DER aggregator when formulating a profitable bidding strategy and schedule sc...
Article
Accurate minutely solar irradiance forecasting is the basis of minute-level photovoltaic (PV) power forecasting. In this paper, a minutely solar irradiance forecasting method based on real-time surface irradiance mapping model is proposed, which is beneficial to achieve higher accuracy in solar power forecasting. First, we extract the red–green–blu...
Article
Customer baseline load (CBL) estimation is very important in demand response (DR) program. Due to the increasing installation of distributed photovoltaic system (DPVS), the load patterns of residential customers become more complex and random. The actual load power of the customer is coupled with the DPVS output power, which makes it more difficult...
Article
Photovoltaic (PV) power generation is an effective means to realize solar energy utilization. Due to the natural characteristics of random fluctuations in solar energy, the applications of PV power such as grid-connected PV power plant, distributed PVs, and building integrated PVs will introduce new characteristics to the generation and load side o...
Article
This paper proposes a meta-heuristic optimization based two-stage residential load pattern clustering (LPC) approach to address two main issues that exist in the most current LPC methods: 1) unreasonable typical load pattern (TLP) extraction; 2) a good clustering should achieve a good balance between the compactness and separation of the formed clu...
Article
Household characteristics play an important role in helping utilities carry out efficient and personalized services. Current methods to obtain such information e.g. survey are usually costly and time-consuming. The widespread installation of smart meters enables the collection of fine-grained residential electricity consumption data, thus making th...
Article
Full-text available
Home energy management systems (HEMSs) enable residential customers to efficiently participate in demand response programs in order to obtain optimal benefits. Traditional HEMSs only manage household electric appliances to reduce the electricity consumption cost while the optimal scheduling of natural gas appliances has been overlooked. Due to the...
Article
In a typical electricity market, the load aggregator (LA) bids in the wholesale market to purchase electricity and meet the expected demand of its customers in the retail market. However, considering the uncertainty of the wholesale market prices (WMPs), the LA has to undertake all the risks arising from the price volatility in the wholesale market...
Article
With the deepening of electricity market reform in China, the competition in the electricity retail market becomes increasingly intense. Electricity retailers (ERs) need to explore new business models to enhance their competitiveness in the retail market. Meanwhile, with the improvement of industrial production and people's living standards, more a...
Article
The motion of cloud over PV power station will directly cause the change of solar irradiance, which indirectly affects the prediction of minute-level PV power. Therefore, the calculation of cloud motion speed is very crucial for PV power forecasting. However, due to the influence of complex cloud motion process, it is very difficult to achieve accu...
Article
Day-ahead electricity price forecasting (DAEPF) plays a very important role in the decision-making optimization of electricity market participants, the dispatch control of independent system operators (ISOs) and the strategy formulation of energy trading. Unified modeling that only fits a single mapping relation between the historical data and futu...
Article
The comprehensive understanding of the residential electricity consumption patterns (ECPs) and how they relate to household characteristics can contribute to energy efficiency improvement and electricity consumption reduction in the residential sector. After recognizing the limitations of current studies (i.e. unreasonable typical ECP (TECP) extrac...
Article
Full-text available
Solar photovoltaic (PV) power forecasting has become an important issue with regard to the power grid in terms of the effective integration of large-scale PV plants. As the main influence factor of PV power generation, solar irradiance and its accurate forecasting are the prerequisite for solar PV power forecasting. However, previous forecasting ap...
Article
Full-text available
Most distributed photovoltaic systems (DPVSs) are normally located behind the meter and are thus invisible to utilities and retailers. The accurate information of the DPVS capacity is very helpful in many aspects. Unfortunately, the capacity information obtained by the existing methods is usually inaccurate due to various reasons, e.g., the existen...
Article
Full-text available
Most current customer baseline load (CBL) estimation methods for incentive-based demand response (DR) rely heavily on historical data and are unable to adapt to the cases when the load patterns (LPs) in the DR event day are not similar enough to those in non-DR days. After the error generation mechanism of current methods is revealed, a synchronous...
Article
Full-text available
Demand response (DR) is a key technology enabling reliable and flexible power system operation more economically and environment-friendly than conventional manners from supply side. Customer baseline load (CBL) estimation is an important issue in the implementation of DR programs for assessing the performance of DR programs and designing economic c...
Article
Full-text available
Accurate cloud image forecasting is very necessary for sky image based solar power forecasting. To provide a means that can track the cloud deformation process and then forecast the cloud shape and position in a future sky image, a cloud image forecasting approach is proposed in this paper. Firstly, the cloud pixels in sky images are identified usi...
Article
Power system reliability faces serious challenges when supply shortage occurs because of unexpected generation or transmission line outages especially during extreme weather conditions. Alternative to conventional approaches that solicit aids from the generation side, operators can now leverage the demand-side resources through a variety of electri...
Conference Paper
Rapid growth of air conditioning (AC) has caused large effect on safe and economical operation of power grid especially in summer. The research on the AC load monitoring is of great significance for improving the stability of power grid. Non-intrusive load monitoring (NILM) is gaining attention due to many attractive features, such as low cost, low...
Conference Paper
It is indispensable for electric power companies to classify the electricity consumption behavior of residential customers, in order to understand the user's personalized demands and provide them with targeted services. K-means Clustering algorithm is one of the most popular methods for grouping the consumption patterns in previous studies. However...
Conference Paper
Operating Statuses Identification (OSI) can help operators to find fault timely and minimize the loss. So it is of great significance for optimal operation of photovoltaic (PV) plants. The loss quantity of electricity (LQOE) is defined as that should have been generated but actually not, which is caused by inverter fault, PV modules fault, dust str...
Conference Paper
To improve the prediction accuracy of photovoltaic (PV) power generation, the temperature of PV modules and its prediction is very important. A short-term step-wise temperature prediction model for PV module based on Support Vector Machine ( SVM) is proposed in this paper. Firstly, the primary impact factors of PV module temperature are determined...

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