The Optimal Energy Flow Strategy of Integrated Energy System Considering Demand Response

Building an integrated energy system with source-load coordination and flexible interaction in the context of dual carbon is an effective path to building a new type of power system. The efficient operation of the integrated energy system can be promoted by establishing incentives such as energy subsidies to reasonably regulate the demand response on the customer side. In this paper, we consider the multi-source demand response on the customer side and study the optimal energy strategy from the perspective of the Integrated Energy Service Company (IESC), specifically involving the construction of the system electricity demand response and heat demand response models. And we propose an IESC optimal energy flow considering the customer’s demand and supply balance game to optimize the IESC. The optimal energy flow strategy of IESC considering the demand response of users is proposed. Finally, the correctness and rationality of the model are verified by simulation analysis.


Introduction
With solar energy, wind energy, and other renewable energy resources vigorously developing, promoting the planning and construction of new energy-based new power systems has become the key to building a modern energy system [1] .However, controlling the total amount of fossil energy, focusing on improving the utilization efficiency, and building a new power system with new energy as the mainstay is an important construction path and development mode to change the traditional energy system, as well as to break the triple barriers of technology, system and market to realize the social value of energy transformation and complementarity [2] .The construction of the integrated energy system can realize the effective coordination of multi-energy complementation and source-grid-load-storage, and take into account energy system security, reliability, and cleanliness.The optimal energy flow strategy of integrated energy systems has become a research hotspot in the energy field because it can meet the diversified energy demand of users by integrating multiple energy resources such as electricity and natural gas in the region [3] .
Si et al.'s work [4] mainly considers the reliability and environmental protection of the integrated energy system and constructed a multi-objective optimal dispatching model.In order to increase the proportional consumption of renewable energy, Zhang et al. [5] propose an integrated energy system planning and scheduling model based on energy hubs and consider the system scheduling and configuration for a typical day of scenic output, but they do not consider the uncertainty of renewable energy generation.In Zhu et al.'s work [6] , the Monte Carlo method is used to simulate the distribution of wind, light, and load to construct a stochastic chance-constrained planning model for integrated energy stations accounting for the uncertainty of wind, light, and load.In Ana et al.'s work [7] , a two-stage stochastic optimization strategy for an integrated energy system is proposed by determining multiple wind power Scenarios and the corresponding probabilities through a probabilistic Scenario method.In Yao and Wang's work [8] , an improved spectral clustering algorithm is used to determine the typical Scenarios of scenery output and construct a two-stage collaborative optimization allocation model of the integrated energy system based on the set of typical Scenarios of scenery.Chen et al. [9] complete the treatment of power uncertainty and load uncertainty to increase the system robustness by constructing a two-level robust optimal dispatch model for microgrids.
In Lin et al.'s work [10] , the grid company achieves master-slave game interaction with multiple customers by developing a demand response incentive subsidy strategy.In Haghifam et al.'s work [11] , a multi-follower grid two-tier planning and scheduling method based on game theory is proposed to maximize the benefits of multiple subjects such as dispatch centers, grid companies, and demand response aggregators.And in Li et al.'s work [12] , a two-stage optimal operation method for regional integrated energy systems considering multiple uncertainties and demand response is proposed by introducing realizable demand response in the day-ahead and intra-day phases, respectively, and switchable demand response to promote energy supply and demand balance.Li et al. [12] considers the demand response based on time-of-day tariff and proposes a two-stage coordinated optimal dispatching method to determine the demand response baseline in the day-ahead stage while optimizing the demand response volume in the intra-day stage, thus improving the operational efficiency of the system.In Li et al.'s work [12] , a two-layer planning and scheduling model is constructed considering the customer-side load demand response, with the upper layer aiming at optimizing the energy supplier's supply-demand balance game and the lower layer aiming at optimizing the regional integrated energy system's supplydemand balance game to develop an optimal scheduling scheme for the supplier's energy system.
However, some of the literature has been carried out to study the optimization of integrated energy system operation, and the design of subsidized demand response incentive mechanisms under the consideration of source-load uncertainty is less frequently carried out.Therefore, in this paper, from the perspective of integrated energy service providers (IESCs), we describe the two-layer game behavior of IESCs and customers based on energy supply and demand balance incentives while considering the source-load uncertainty of integrated energy systems, and construct an optimal scheduling model of integrated energy systems with multi-source demand response.Finally, the effects of customer demand response on the IESC energy purchase strategy, energy conversion equipment output plan, and IESC supply-demand balance game optimization under different Scenarios are obtained through simulation, which verifies the effectiveness of the proposed model.

IESC decision structure
IESC is equipped with its own battery, gas storage, generator set, and CHP unit.It exports electrical and thermal energy to customers by purchasing responsive energy in the electricity and natural gas markets and by combining it with its own energy conversion equipment.The gas turbine (GF) converts the incoming natural gas into heat, while the CHP unit acts as a coupling device between electricity and natural gas, converting the incoming natural gas into electricity and heat.On the customer side, the customer can respond to the demand and supply balance of the electricity and natural gas markets for demand response, and the IESC will send the optimal form of energy use and the energy conversion strategy of each piece of equipment directly to the customer through the energy controller to achieve the optimization of the supply and demand balance game of the IESC.
, , , denotes the baseline electricity and heat consumption of the user in the time period t.And Equations ( 2) and ( 3) indicate that the total amount of electricity and heat consumption of the user remains unchanged during the day.denote time period t charging and discharging binary variables, and Equation (11) indicates that the battery can only be charged or discharged in a single time period.

Gas storage unit constraints
min max , max denote the amount of natural gas stored in the storage tank from the amount of natural gas purchased by IESC from the gas grid in time period t , the amount of natural gas abandoned by the storage tank to the CHP unit, and the amount of natural gas released from the storage tank to the gas boiler.max ch t G ， and max dis t G ， denote the boundary constraints on the maximum amount of natural gas charged and discharged from the storage tank in time period t . , and , denote the binary variables for filling and discharging in time period t .Equation (19) denotes that the storage tank can only be charged or discharged in a single time period.

Diesel generator constraints
The IESC is equipped with a diesel generator set to provide a certain amount of electrical power.The constraint is as follows: is the maximum generation output constraint of the generating unit.

Gas turbine constraints
By burning natural gas, the gas turbine generates a large amount of high-temperature heat energy and delivers it to the heat consumer.The constraint is as follows: where h gf η denotes the heat conversion efficiency of the gas boiler; max gf G is the maximum natural gas input to the gas turbine.

CHP unit constraints
The cogeneration unit can output electrical and thermal energy by inputting natural gas and performing energy conversion with the following constraints: ) where e chp η and h η denote the gas-to-power efficiency and gas-to-heat efficiency of the CHP unit; chp t G is the amount of natural gas delivered to the CHP in time period t ; and max chp G is the maximum amount of natural gas delivered to the CHP in each time period.

System equilibrium constraint
The energy balance constraints for each IESC device are as follows: denotes the electricity supplied directly to the customer in wind power.

Parameter setting
The parameter devices in this paper are shown in Table 1 below.The initial energy use curve and the projected wind power output curve in this paper are shown in Figure 2, and the electricity and natural gas energy supply and demand are shown in Figure 3.  Scenario 1 has a supply-demand balance benefit margin of 70% and Scenario 2 has a supply-demand balance benefit margin of 65% for Scenario 1.Compared with Scenario 1, Scenario 2 reduces the cost of electricity by shifting all of the transferable load to the time when the electricity market price is high, as opposed to reducing the electricity use when the electricity market price is high, and the heat load is used in the same way as the electricity load, thus reducing the cost of heat energy by cutting the peak and filling the valley.At the same time, because IESC is equipped with storage battery and gas storage tank, it can store electricity and gas when energy supply and demand are not tight, and release electricity and gas when energy supply and demand are tight, similar to the strategy of demand response, the energy storage device transfers energy from the time when supply and demand are not tight to the time when supply and demand are tight, which is equivalent to reducing the cost of energy load.Although the reduction in electricity and heat use at the peak of electricity and heat use will reduce the utility of electricity and heat use, the reduction in the cost of electricity and heat use will have a major impact, so IESC's supply-demand balance in Case 1 will be better than that in Case 2.

Effect of gas to electricity efficiency improvement on optimization results
When the gas-to-electric efficiency and gas-to-thermal efficiency of CHP units will be continuous with the progress of production technology, this section assumes 3 scenarios of Scenario 1 (gas-to-electric efficiency 0.38), Scenario 2 (gas-to-electric efficiency 0.40), and Scenario 3 (gas-to-electric efficiency 0.42) to analyze the impact of gas-to-electric efficiency improvement on IESC strategy as well as profit.Table 2 below shows the IESC's supply-demand balance benefit margin of merit for the 3 scenarios.As can be seen from the graph, the electrical output of the CHP unit is increasing as the gas-toelectricity efficiency of the CHP unit continues to increase.This indicates that more electrical energy will be converted per unit of natural gas input, i.e., the reduction of electrical energy loss per unit.This means that the overall efficiency of the IESC is increasing, and therefore the IESC's supply and demand balance game superiority rate is increasing.

Conclusion
In this paper, an IESC optimization model considering the demand response of electricity and heat demand response of users in IESC is established.The simulation analysis verifies the correctness and reasonableness of the model.From the conclusion, it can be seen that with the energy storage equipment, the demand response on the customer side will realize the reasonable use of energy by the customers and the peak and valley reduction of the load, and the gas-to-electricity efficiency of CHP can continuously improve the electric output of CHP, reduce the energy loss, and improve the winning rate of the IESC supply-demand balance game.

Figure 2 .Figure 3 .
Figure 2. User's initial energy consumption and predicted wind power output

Figure 10
Figure 10 below shows the generation output of the CHP unit for the three scenarios.

Figure 10 .
Figure 10.The power generation output of CHP under three scenarios power purchased from the grid by IESC in time period t and the electrical energy stored in the battery from the wind.max