A simple model was built to predict the national and regional electricity demand by sectors under China’s new normal situation. In the model, the data dimensionality reduction method and the Grey model (GM(1,1)) were combined and adopted to disaggregate the national economic growth rate into regional levels and forecast each region’s contribution rate to the national economic growth and regional industrial structure. Then, a bottom–up accounting model that considered the impacts of regional industrial structure transformation, regional energy efficiency, and regional household electric consumption was built to predict national and regional electric demand. Based on the predicted values, this paper analyzed the spatial changes in electric demand, and our results indicate the following. Firstly, the proposed model has high accuracy in national electricity demand prediction: the relative error in 2017 and 2018 was 2.90% and 2.60%, respectively. Secondly, China’s electric demand will not peak before 2025, and it is estimated to be between 7772.16 and 8458.85 billion kW·h in 2025, which is an increase of 31.28–42.88% compared with the total electricity consumption in 2016. The proportion of electricity demand in the mid-west regions will increase, while the eastern region will continue to be the country’s load center. Thirdly, under China’s new normal, households and the tertiary industry will be the main driving forces behind the increases in electric demand. Lastly, the drop in China’s economy under the new normal will lead to a decline in the total electricity demand, but it will not evidently change the electricity consumption share of the primary industry, secondary industry, tertiary industry, and household sector.

One of the key aspects of developing a regional renewable energy plan involves incorporating national renewable energy targets into provincial targets whilst including interim phase targets and technology targets. This paper proposes a renewable energy model based on national targets 'decomposed' into regional targets that incorporate technological processes and environmental conditions at the individual project level. At the project level, resource potential is allocated into individual projects based on the current technological level. The available resources and renewable energy generation of each project changes as the technology evolves and the environmental conditions change over time. The model can be adjusted according to actual needs of each region; thus policymakers can establish the respective targets for power, heating, oil and gas as well as renewable energy based on the regional context. Overall, the total national target can be decomposed into regional targets and technological targets. This paper proposes long-term regional development targets for renewable energy based on the total supply curve for renewable energy in different periods. Fujian province will be applied as an example to validate the target decomposition model of renewable energy targets of various regions and technologies in China. The model projected that the economic capacity of renewable energy generation in Fujian province for dam hydropower, offshore wind power, onshore wind power, photovoltaic power and waste incineration power is expected to reach 4748. MW, 4036. MW, 3581. MW, 2663. MW and 766. MW, respectively by 2025. The research result could have wider implications such as achieving GHG emissions reductions targets, addressing environmental concerns, providing high tech jobs within the region and contributing to energy security at the local and national level.

It is crucial that China's renewable energy national target in 2020 is effectively decomposed into respective period targets at the provincial level. In order to resolve problems arising from combining the national and local renewable energy development plan, a total target and period target decomposition model of renewable energy is proposed which considers the resource distribution and energy consumption of different provinces as well as the development characteristics of various renewable energy industries. In the model, the total proposed target is comprised of three shares: basic share, fixed share and floating share target. The target distributed for each province is then determined by the preference relation. That is, when total renewable energy target is distributed, the central government is more concerned about resources potential or energy consumption. Additionally, the growth models for various renewable energy industries are presented, and the period targets of renewable energy in various provinces are proposed in line with regional economic development targets. In order to verify whether the energy target can be achieved, only wind power, solar power, and hydropower are considered in this study. To convenient to assess the performance of local government, the two year period is chosen as an evaluation cycle in the paper. The renewable energy targets per two-year period for each province are calculated based on the overall national renewable energy target, energy requirements and resources distribution. Setting provincial period targets will help policy makers to better implement and supervise the overall renewable energy plan.

Designing an electricity price mechanism that can reduce the power generators' market power as well as pollution emissions has been the key to deepening the reform of the electricity market. Based on the theory of signal transduction, this paper analyzes the main electricity pricing mechanism in the present electricity market and holds that the interference signal is the important factor in evaluating electricity pricing mechanism. As a result of the study, an incentive electricity pricing mechanism was proposed based on the signal transduction to coordinate the electric power and pollution emission. Two measures are adopted in the model. First, semi-random matching between the generator and the power purchaser, which can interfere with the tacit collusion signals among generators, is carried out in the electricity market. Subsequently, the electricity products are divided into two categories according to the emissions per MW h and different electric power markets are set respectively. The government adopts the corresponding incentive strategy to encourage the generators to reduce pollution emissions in the different electric power markets. The mechanism interfere the tacit collusion signal among generators by semi-randomly matching and distinguishing the difference of electric power products. Therefore, the generator can be persuaded to reduce the market power and pollution emissions.

At present, designing a proper bidding mechanism to decrease the generators' market power is considered to be one of the key approaches to deepen the reform of the electricity market. Based on the signaling game theory, the paper analyzes the main electricity bidding mechanisms in the electricity auction markets and considers the degree of information disturbance as an important factor for evaluating bidding mechanisms. Under the above studies, an incentive electricity bidding mechanism defined as the Generator Semi-randomized Matching (GSM) mechanism is proposed. In order to verify the new bidding mechanism, this paper uses the Swarm platform to develop a simulation model based on the multi-agents. In the simulation model, the generators and purchasers use the partly superior study strategy to adjust their price and their electricity quantity. Then, the paper examines a simulation experiment of the GSM bidding mechanism and compares it to a simulation of the High-Low Matching (HLM) bidding mechanism. According to the simulation results, several conclusions can be drawn when comparing the proposed GSM bidding mechanism to the equilibrium state of HLM: the clearing price decreases, the total transaction volume increases, the profits of electricity generators decreases, and the overall benefits of purchasers increases. Index Terms-signaling game; semi-randomized matching; high-low match.