Simulation And Optimization Of Natural Gas Time-of-use Pricing Based On Demand Response

City gas pipeline network, the public facility of a city, has become the foundation of social stability and economic development. With the sharply development of natural gas companies at home and abroad, pipeline network covers more and more areas and pipeline network consumers rise in kinds and numbers with increasingly dependence on natural gas. As the natural gas industry develops rapidly, gas famine has been caused by the grossly inadequate of natural gas and industries’ growing dependence on natural gas. According to the scarce resources in both time and space, complicated product cost and difference in peak load regulation, it is urgent to study natural gas price for different consumers in different period, to allocate resources reasonably and to coordinate the interests of stakeholders feasibly. These measures can relieve city gas famine and stipulate natural gas industry to develop healthily.By invesgating current terminal market of natural gas, the study analyses the price demand elasticity and the user consumption psychology of natural gas under time-of-use pricing method from the view of systematic science and economics. Then the study adopts Agent-Based Modeling and Simulation (ABMS) and Intelligence Algorithm to build the simulation and optimization model for terminal market of natural gas, which imitates how intensive city natural gas consumers will respond to price that changes with time and shows the emerging characteristics of the complicated economic system dynamically. The main research contents are as follows:(1) Natural gas Agent-Based System Model for time-of-use pricing. The study firstly analyses objects, implementing principles and control measures of time-of-use pricing; secondly abstracts the core stakeholders of terminal market based on city gas pipeline structure and on terminal market characteristics; thirdly builds up natural gas Agent-Based System Model for time-of-use pricing; finally according to ABMS, clarifies the agents included in a system and the coordination among each agent.(2) Time-of-use pricing simulation study based on demand response. Natural gas value varies from consumer to consumer, so multiple demand responses should be taken into account to build Agent-Based Simulation Model and the study mainly considers industry and citizen use, which are the most typical use. Then the study uses price elasticity and consumption psychology of demand response rule respectively to build Agent-Based Simulation Model, to simulate each stakeholder’s evolution characteristics under peak-valley price and to evaluate time-of-use pricing policy.(3) Optimization study on time-of-use pricing based on Intelligence Algorithm. According to Simulation Model, the study firstly transfers system to multi-objective optimization model; secondly adopts GA-Pareto method to conduct optimization calculation of time-of-use pricing system; thirdly selects a set of optimal time peak and valley to support time-of-use pricing policy.The simulation analysis of time-of-use pricing shows that the citizen consumers are the main factors causing gas pipeline network instable but that industrial consumers are the benefit source of gas companies. After implementing time-of-use pricing, citizens cannot change the habits and the way to use natural gas, so how intensive they will response to price change is hard to determine. On the other hand, the gas cost for industrial consumers is huge, so they will response to time-of-use pricing more or less, eventually leading to gas companies’ revenue to decline. In this way, the government can give gas companies some subsidies to simulate them to participate in time-of-use pricing policy. What is more, the optimization study of time-of-use pricing shows that:different kinds of users in different demand response models, there exists an optimal peak and valley price ratio to optimize the system target. The study results show that the experimental data supports the assessment and decision of natural gas pricing policy.