Jianxiao Wang

Steady-State Power Flow Model of Energy Router Embedded AC Network and Its Application in Optimizing Power System Operation

The energy router is an emerging device concept that is based on an advanced power electronic technique. It is able to realize flexible and dynamic electric power distribution in power systems analogous to the function of information routers in the Internet. It is of great interest to investigate how the energy router can be used to optimize power system operation. This paper formulates the steady-state power flow model of the energy router embedded system network and the related optimal power flow formulation. The role of the energy router in providing extra flexibility to optimize the system operation is studied. Case studies are carried out on a modified IEEE RTS-79 system and a modified IEEE 118 bus system with the energy router. The results show that the energy router is able to optimize the operation of the power system through controlling the power injections and voltage of ports of the energy router. Operating objective such as adjusting branch power flow, improving bus voltage, and reducing active power losses of the grid can be reached under different objective functions.

Optimal Planning Strategy for Distributed Energy Resources Considering Structural Transmission Cost Allocation

A well-designed transmission cost allocation (TCA) scheme is able to reflect each consumer’s contributions to the actual usage of transmission assets, thereby generating fair price signals to guide consumers in making investments. In this paper, an optimal planning strategy and a model for distributed energy resources (DERs) are formulated, considering structural TCA. In the proposed TCA scheme, the transmission costs are identified according to the actual usage of transmission assets under different conditions. The effects of DERs on TCA are considered. Because the DERs have a fast-response capability at each time slot, DERs are able to provide power support for the bulk power system in an emergency and thus can efficiently reduce the peak loads of the system and relieve the congestion of transmission assets. This grid-friendly manner allows lower transmission prices, thereby decreasing the DER operators’ transmission costs. Therefore, by strategically investing in and operating DERs, consumers can reduce their electricity energy cost and transmission cost and maximize long-term revenue. To address the uncertainties of load growth and distributed energy fluctuation, stochastic programming is adopted. Restricted by the non-analytical mapping between transmission prices and planning strategies, an algorithm is presented to iteratively solve the problem. Case studies based on a 3-bus test system and IEEE 118-bus system with various DERs verify the effectiveness of the proposed scheme.

Exploring Key Weather Factors from Analytical Modeling toward Improved Solar Power Forecasting

Accurate solar power forecasting plays a critical role in ensuring the reliable and economic operation of power grids. Most of existing literature directly uses available weather conditions as input features, which might ignore some key weather factors and the coupling among weather conditions. Therefore, a novel solar power forecasting approach is proposed in this paper by exploring key weather factors from photovoltaic (PV) analytical modeling. The proposed approach is composed of three engines: 1) analytical modeling of PV systems; 2) machine learning methods for mapping weather features with solar power; and 3) a deviation analysis for solar power forecast adjustment. In contrast to the existing research that directly uses available weather conditions, this paper explores the physical knowledge from PV models. Different irradiance components and PV cell temperatures are derived from PV analytical modeling. These weather features are used to reformulate the input of machine learning methods, which helps achieve a better forecasting performance. Moreover, based on the historical forecasting deviations, a compensation term is presented to adjust the solar power forecast. Case studies based on measured datasets from PV systems in Australia demonstrate that the forecasting performance can be highly improved by taking advantage of the key weather features derived from PV models.

Evaluating the impacts of flexible ramping products on the market equilibrium

Flexible ramping products, which are bid-based products recently proposed in CAISO and MISO markets, are designed to provide transparent economic incentives for flexible resources in case of the increasingly high penetration of renewables. In order to evaluate the impacts of flexible ramping products on the market equilibrium, a multi-period Nash-Cournot equilibrium model is established and the potential-function based solution method is adopted to transform the multi-individual profit-maximization problems into an integrated single-level optimization model. Numerical examples with several cases are also implemented and compared to show the impacts of the new market-based products on energy prices and the integration of renewables.