Energy is the bridge connecting the economy and the environment and electric energy is an important guarantee for social production. In order to respond to the national dual-carbon goals, a new power system is being constructed. Effective carbon emission forecasts of power energy are essential to achieve a significant guarantee for low carbon and clean production of electric power energy. We analyzed the influencing factors of carbon emissions, such as population, per capita gross domestic product (GDP), urbanization rate, industrial structure, energy consumption, energy structure, regional electrification rate, and degree of opening to the outside world. The original Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model was improved, and the above influencing factors were incorporated into the model for modeling analysis. The ridge regression algorithm was adopted to analyze the biased estimation of historical data. The carbon emission prediction model of Shanghai electric power and energy based on elastic relationship was established. According to the “14th Five-Year” development plan for the Shanghai area, we set up the impact factor forecast under different scenarios to substitute into the forecast models. The new model can effectively assess the carbon emissions of the power sector in Shanghai in the future.

As a retailer between the energy suppliers and end users, the integrated energy service provider (IESP) can effectively coordinate the energy supply end and the energy use end by setting energy prices and energy management. Because most of the current research focuses on the pricing of electricity retailers, there are few studies on IESP energy pricing and management, which are still at the initial stage. At the same time, the existing research often does not consider the impact of demand response (DR) and uncertainties, such as natural gas and electricity wholesale prices, on the pricing of IESP. It is necessary to model the DR and uncertainties in the integrated energy system. Aiming at the inadequacy of the existing research and to address the energy pricing and management of IESP, this paper develops a two-stage stochastic hierarchical framework, which comprehensively considers the DR strategy of the user end, characteristics of the electricity/gas/heat storage and the uncertainties of electricity and gas wholesale prices. The proposed hierarchical model for energy pricing and management is a two-layer model: the upper layer is the problem of maximizing the benefits of IESP, and the lower layer is the problem of minimizing the energy cost of user agents. Through the complementary transformation, the linearization method and the strong duality principle in the optimization theory, the model is transformed into a mixed-integer linear programing (MILP) problem, which can be easily solved by the off-shelf commercial solver. Finally, the simulation results are provided to demonstrate the interactive operation between the IESP and user agent through energy prices setting, DR strategy and energy management.

This article aims to explore the development trends of the electricity industry in light of dual carbon goals. It analyzes the role of these goals in promoting electricity education and addresses the challenges associated with their implementation. Specifically, the article emphasizes the significance of integrating renewable energy and low-carbon technologies. It investigates the effective introduction of emerging technologies and concepts into electricity education, with the goal of nurturing professionals capable of meeting the future demands of the power industry. Additionally, it delves into the close collaboration between electricity education and the electricity industry, highlighting their joint efforts in carbon emission reduction within the power system. The primary objective of this research is to offer guidance for the reform and development of electricity education, playing a vital role in facilitating the low-carbon transformation of the power industry.

To address the challenges in new power systems, such as wind and photovoltaic curtailment and insufficient energy storage incentives, caused by imbalances in the regulation of power supply and demand, the academic community has proposed the substitute power product (SPP) market, which is based on the trading of prescribed generation shapes over defined periods. However, there is currently no literature addressing the optimization strategies for market participants within the SPP market trading model. To fill this gap, this study introduces, for the first time, an energy storage planning and optimization operation strategy for wind and photovoltaic energy stations within this market framework. First, the basic trading process and related concepts of the SPP market are introduced. Then, based on the operational logic of the SPP market, a multi-stage energy storage planning and operation strategy is proposed for wind and photovoltaic stations. This strategy integrates a two-level model with a multi-scenario stochastic planning model to optimize the storage capacity and power allocation of renewable energy stations under uncertainty. Subsequently, relevant metrics for the SPP market are introduced, and these metrics are used to quantitatively analyze the feasibility of cooperation among different renewable energy stakeholders in the market. Finally, the impact of regulation power prices on the decision-making of renewable energy stations participating in the SPP market is explored. The findings of this study provide new energy producers with a preliminary optimization solution for energy storage configuration and operation under the new trading model, promoting their participation in the SPP market and contributing to the acceleration of the low-carbon transformation of power systems.