CN108629470A - System capacity management of providing multiple forms of energy to complement each other based on non-cooperative game is run with optimization - Google Patents

Abstract

The present invention aims at the multi-functional complementary system under the background of the energy Internet, regards the system operator and the user as two different actors, and builds the architecture of the combined heat and power multi-functional complementary system based on the centralized interconnected energy hub. Analyze a variety of energy supply, energy storage, and energy-using equipment in the park, establish a refined energy flow model for various equipment, and take into account user comfort to formulate detailed plans for electric vehicles, household appliances, hot water loads, and air heat loads. scheduling strategy. A master-slave game model of the multi-functional complementary system is established. The model aims at minimizing the user's energy consumption cost and maximizing the operator's profit, and comprehensively considers the mutual coordination of the above-mentioned supply, storage, and energy-consuming equipment. Through simulation verification, the model built by the present invention can achieve the purpose of fully tapping user autonomy, effectively taking into account the interests and needs of both parties, and realizing the mutual benefit of energy network interconnection.

Description

Energy management and optimal operation of multi-energy complementary system based on non-cooperative game

technical field

The invention belongs to the field of multi-energy complementary system operation, and relates to the operation control strategy of operators and users.

Background technique

The increasingly serious climate deterioration and energy crisis have put forward new requirements for promoting energy revolution and improving energy utilization efficiency. According to statistics, my country currently has nearly 2,000 national-level and provincial-level development zones of various types. The demand for energy in the parks is huge, and the regulation and management of loads has greater flexibility and controllability. According to the actual situation, the park-type basic energy Internet built with the park as a unit provides a way to realize the efficient and complementary utilization of various energy sources.

In recent years, scholars at home and abroad have conducted extensive research on combined heat and power systems with micro gas turbines as the core equipment, and there are two main problems:

(1) The lack of consideration of the details of electricity and heat load scheduling is not conducive to subsequent promotion.

(2) At present, there are many research results on centralized optimization dominated by a single entity. However, in a competitive and open market environment, a variety of operating entities have emerged in the power system, including electricity sales companies, agents, and micro-grid operators. merchants, users, etc. Due to the different benefit requirements of various stakeholders, centralized optimization is difficult to take into account the interests of multiple subjects, and decentralized optimization, from the perspective of satisfying the dynamic balance of the interests of multiple subjects, has become a solution to such problems with its higher flexibility and adaptability. effective method. However, the existing research results focus on optimization from the perspective of operators, ignoring the interests and needs of users, resulting in low participation. However, with the improvement of the power market and the development of communication and metering technologies, individual users are more intelligent and autonomous, and are no longer simply recipients of the operator's strategy, but users can also participate in the optimized operation of the system as one of the main bodies. . As different decision-making subjects, operators and users have more complex interests and competition. Studying the interaction mechanism to realize the positive interaction between the energy side and the load side is an effective way to achieve energy efficiency and environmental benefit optimization.

Contents of the invention

On the energy demand side, household energy consumption accounts for a considerable proportion. This invention aims at the multi-energy complementary system under the background of energy Internet, and regards the system operator and the user as two different actors, and builds it based on a centralized interconnected energy hub. Architecture of combined heat and power multi-energy complementary system. Based on this framework, various energy supply, energy storage, and energy consumption equipment in the park are analyzed, and refined energy flow models for various equipment are established, and user comfort is taken into account for electric vehicles, household appliances, hot water loads, air Heat load formulate detailed scheduling operation strategy. A master-slave game model of the multi-energy complementary system is established. The model aims to minimize the energy consumption cost of the user and maximize the operator's profit, and comprehensively consider the mutual coordination of the above-mentioned energy supply, storage, and energy consumption equipment.

In order to achieve the above object, the technical solution proposed by the present invention is based on the non-cooperative game-based energy management and optimal operation of the household type multi-energy complementary system, which is characterized in that it includes the following steps:

Step 1: Based on the centralized and interconnected energy hub, build the architecture of combined heat and power home-type multi-energy complementary system, and analyze the operation mode of the system:

(1) The operator's forecast center predicts the temperature and output of photovoltaic units in each time period of the next day in advance.

(2) A few days ago, the operator pre-arranged the output of gas turbines and gas boilers according to the heat load demand of users. For the electricity load, the priority is met by photovoltaic and gas turbine units, and a power purchase contract is signed with the upper-level grid to determine the amount of electricity purchased for each period, and the real-time electricity price for each period of the next day is released to the user. In real-time operation, when the system power supply is surplus (insufficient), operators can also sell (buy) power to the real-time market.

(3) During the day, the home energy management system (HEMS) installed in the user's home automatically selects the optimal use of various household appliances based on the electricity price information released by the operator, taking into account the comfort of the user. Electric strategy to control the switching of various loads that can be shifted in translation; on the other hand, on the premise of satisfying the temperature comfort, optimize the thermal load at each time period.

Step 2: Establish the output model and cost model of the micro gas turbine with the constraints of related equipment operation requirements;

Step 3: Taking into account the user's comfort constraints, convert the start-up time of the translatable load to be optimized into a 0, 1 vector representing its start-stop state, so as to formulate the operation control strategy of the translatable load; establish the PMV index for temperature comfort Quantify the temperature comfort degree, and establish the model of space heat load and hot water load with the temperature comfort degree as the constraint.

Step 4: Establish a master-slave game model between operators and users, build an operator revenue model and a user revenue model, and divide the model into upper and lower sub-modules. The upper layer takes the maximum operator revenue as the objective function, and the lower layer ensures its own Under the premise of comfort constraints, the objective function is to minimize the user's payment cost, and the chaotic fish swarm algorithm and the YALMIP toolbox in MATLAB are used to solve the problem, and the strategy set of both parties at the equilibrium point is obtained.

Description of drawings

1. Figure 1 is a structural diagram of a multi-energy complementary system

2. Figure 2 is an optimization flow chart

3. Figure 3 shows the hot water load demand of a single resident user in a day

4. Figure 4 is the flexible electrical load parameters

5. Figure 5 shows the thermal resistance, temperature, specific heat capacity, water storage tank volume, and water density parameters

6. Figure 6 is the optimization result of hot water temperature and room temperature

7. Figure 7 is the real-time electricity price set by the operator

8. Figure 8 is the rigid load of the user

9. Figure 9 is the optimization result of the translatable load.

Detailed ways

Step 1: Establish the architecture of the combined heat and power multi-energy complementary system based on the energy hub of centralized interconnection, and analyze the operation mode of the system:

The operating mode is as follows:

(1) The operator's forecast center predicts the temperature and output of photovoltaic units in each time period of the next day in advance.

(2) A few days ago, the operator pre-arranged the output of gas turbines and gas boilers according to the heat load demand of users. For the electricity load, the priority is met by photovoltaic and gas turbine units, and a power purchase contract is signed with the upper-level grid to determine the amount of power purchased in each period, and the electricity price information for each period of the next day is released to users. In real-time operation, when the system power supply is surplus (insufficient), operators can also sell (buy) power to the real-time market.

(3) During the day, the home energy management system (HEMS) installed in the user's home, on the one hand, releases energy price information (including real-time electricity prices and heating prices) according to the operator, and automatically selects The optimal power consumption strategy for various household appliances controls the switching of various loads that can be shifted in translation; on the other hand, on the premise of meeting the temperature comfort, optimize the heat load at each time period.

Step 2: Establish a micro gas turbine output model and cost model with operating requirements as constraints:

The micro gas turbine realizes the integration of power generation and heat supply (heating and hot water supply) by utilizing the high-temperature waste heat generated in the power generation process. The load demands are equal, and when the gas turbine capacity is insufficient, it will be met by the gas boiler.

(1) Output model

The mathematical model describing the output of gas turbine is:

(1)

In the formula, Q _MT(t) is the exhaust waste heat of the gas turbine at time t; is the heat dissipation loss coefficient of the gas turbine; P _e (t) is the electric power output by the gas turbine at time t; Q _he (t) is the heating capacity provided by the waste heat of the gas turbine flue gas at time t; K _he is the heating coefficient of the bromine cooler; V _MT is the amount of natural gas consumed by the gas turbine; Δt is the operating time of the gas turbine; L is the calorific value of natural gas, which is taken as 9.7kWh/m ³ in this paper.

(2) Cost model

The operating cost of a gas turbine is mainly the cost of purchasing gas, and the expression of gas cost is:

(2)

In the formula, rgas is the unit price of natural gas, and R is the calorific value of natural gas.

(3) Constraints

Gas turbine equipment capacity constraints:

(3)

Gas Turbine Ramp Rate Constraints

(4)

Step 3: Establish the calculation of household appliances, hot water load, and air heat load taking into account user comfort.

To formulate a detailed scheduling operation strategy:

The electric load model and control strategy are as follows:

According to the load response characteristics, it can be divided into two categories: 1) Uninterruptible load, which mainly refers to lighting, refrigerators, televisions and other equipment that meet people's basic daily needs. Can have a serious impact on user comfort and is insensitive to changes in electricity prices. 2) The load can be moved in parallel, and it can not be interrupted after starting, but the start can be delayed without affecting the shape of the load. The start time of this type of load generally depends on the user's electricity consumption mode and living habits, and is sensitive to changes in electricity prices. Includes washer, dryer, dishwasher and more. By planning the start-up time of this type of load, the purpose of reducing electricity cost while satisfying user comfort can be achieved. Therefore, the present invention mainly takes the translational load as the optimization object. The power consumption characteristics of the translatable load are modeled as:

For the translatable load L _i , let the continuous running time period be H _i , and the optional running time period is [ t start i, t end i]. The variable to be optimized for the translational load is the actual start time t _i ∈ [ t start i, t end i- H _i +1], which is transformed into a start-stop state vector x t i ∈ { 0,1}, which respectively represent the closed and open states of the i -th type of translatable load in the t period, so that the total load in each period can be determined:

(5)

In the formula, _NSAL is the total number of types of loads that can be shifted; P ^t _i is the power of loads of type i that can be shifted during period t ; P ^t _b ^is the power of loads that cannot be shifted during period t ; .

Translatable loads need to meet conditions such as uninterruptible constraints and operating power constraints, which are expressed as follows:

(1) Uninterruptible constraints:

(6)

In the formula, τ is the start-up time after load optimization of type i. The above constraints indicate that once the load is turned on, it needs to run continuously for at least H _i periods of time.

(2) Operating power constraints:

(7)

In the formula, p ⁱ _N is the rated power of the i-type load, p ⁱ _min and p ⁱ _max are the minimum and maximum operating power of the i-type load, respectively. The above constraints indicate that each type of load has a certain power consumption limit. For washing machines, dishwashers and other equipment, once they start working, the power consumption reaches the rated value, and when the equipment stops working, the power is 0.

(3) Transferable time constraints:

(8)

It is worth noting that although different households have different load characteristics and electricity consumption habits, users can set the use time range of electrical appliances according to their own needs, so as to meet the individual needs of users.

The air heat load model considering the comfort level is as follows:

According to the relationship between the given outdoor ambient temperature, indoor temperature, and the heating power required to maintain room temperature and the corresponding parameter settings, the constraint relationship between variables is shown in formula (9):

(9)

In the formula, T _in is the indoor temperature, C _air is the equivalent heat capacity of air (kWh/℃), R is the equivalent thermal resistance of building materials (℃/kW), is the simulation time interval (h), T _out is the outdoor temperature, and H _air is the air heating power.

Establish the PMV (predicted mean vote) index to quantify the temperature comfort, and its calculation formula is:

(10)

When the PMV value is 0, the temperature comfort is the highest, and the corresponding optimal temperature is 26°C. The PMV range given by ISO 7730 should be between -1 and 1. Therefore, the indoor temperature that satisfies the comfort constraint should be It is 24.8°C~27.3°C.

The hot water load model considering comfort is as follows:

Assuming that the water in the heat storage tank is always full, if there is a certain amount of hot water output, there must be an equal amount of cold water to supplement. According to the second law of thermodynamics, the relationship between hot water temperature, cold water volume and the heat required to maintain the required water temperature is obtained as formula (11):

(11)

In the formula, T _ws is the temperature of the hot water in the heat storage tank, ℃; C _water , V _total , and ρ _w are the specific heat capacity of water, the water storage capacity of the hot water tank, and the density of water; The cold water temperature, ℃, H _water is the heating power supplied to the water storage tank.

The hot water load comfort constraints are as follows:

(12)

In the formula, Tmin ws and Tmax ws are the lower limit and upper limit of the optimum temperature of hot water, in °C; Tmin in and Tmax in are the lower limit and upper limit of the indoor optimum temperature, respectively, in °C.

Step 4: Establish a master-slave game model between operators and users, and divide the model into upper and lower sub-modules. The upper layer takes the operator’s maximum income as the objective function, and the lower layer takes the user’s minimum payment as the objective function, combined with chaotic fish shoals The algorithm is solved with the YALMIP toolbox in MATLAB, and the strategy set of both sides of the game at the equilibrium point is obtained:

The upper-level game model takes the highest profit of the operator as the optimization goal, and the decision variables are the optimized electricity price for 24 periods, the electricity purchased from the upper-level power grid, and the amount of natural gas purchased:

Maximize profit:

(13)

In the formula, c _t is the electricity price of period t set by Energy Hub; C ^ahead _t is the electricity price of day-ahead market period t; E ^ahead _t is the contract power of day-ahead market period t; G _t is the natural gas purchase volume of period t; p _gas is The price of natural gas; E ^real+ _t and E ^real- _t are the electricity purchased or sold from the real-time market in time period t, respectively;

Constraint 1: Electricity Price Constraint

Constraint 2: Power Balance Constraints

Constraint 3: Real-time market trading electricity constraints

The lower-level game model takes the lowest household energy consumption cost paid by users as the optimization goal, and the decision variables are the optimal running time of each type of load, the indoor ambient temperature T _in (°C), and the hot water temperature T _ws (°C). The user's payment function in a scheduling cycle is:

(14)

in,

(11)

(12)

In the formula, ht _is the unit heating fee that the user needs to pay to the operator.

Case analysis

In addition to the photovoltaic power supply, the multi-energy complementary system also includes a combined heat and power micro gas turbine and a waste heat boiler. The application of the model of the present invention in the above calculation example is realized by using MATLAB programming.

An analysis of a typical winter day for a household user is carried out to illustrate the essential features of the proposed method of the present invention. Assume that the power grid company adopts the time-of-use electricity price mechanism between peak and valley. The price of natural gas is 2.7 yuan/m ³ , and the hot water load curve and rigid load curve are shown in Figure (3) and Figure (8). The flexible load parameters are shown in Fig. (9). The thermal resistance of building materials, the upper and lower limits of hot water temperature, the temperature of cold water, the thermal resistance of air, the specific heat capacity of water, and the volume of water storage tanks are shown in Figure (5). The research object of the present invention is a residential quarter with 100 households.

The optimized result of the game-optimized time-of-use electricity price model used in the present invention is shown in Figure (7). At this time, the operator's power generation cost is 300.15 yuan, the profit is 900.53 yuan, and the user payment fee is 1200.68 yuan. Considering the user's comfort constraints, for example, the minimum running time of the washing machine is 60 minutes, and the running time is limited to 7-11 hours. During this period, the electricity price is the lowest at 11 o'clock, so the working time of the washing machine is arranged to be 11 hours.

Claims (7)

1. The multi-energy complementary system energy management and optimal operation based on non-cooperative game is characterized in that the method comprises the following three steps: Step 1: Establish a framework for building a home-based multi-energy complementary system based on centralized interconnection of energy hubs, and analyze the operating mode of the system: Step 2: Establish a scheduling model that takes the operating requirements of related equipment as constraints: Step 3: Establish a detailed scheduling operation strategy for household appliances, hot water loads, and air heat loads, taking into account user comfort; Step 4: Establish a master-slave game model between the operator and the user, and obtain the strategy set of both parties at the equilibrium point. 2. According to the method of step 1 in right 1, its main feature is the operation mode of the home-type multi-energy complementary system: The operator's forecast center predicts the temperature and output of photovoltaic units in each time period of the next day; before the day, the operator pre-arranges the output of gas turbines and gas boilers according to the heat load demand of users. , and sign a power purchase contract with the upper-level power grid to determine the power purchase in each time period, and release the real-time electricity price for each time period of the next day to the user. The market sells (purchases) electric energy; during the day, the home energy management system (HEMS) installed in the user's home automatically selects various household appliances based on the electricity price information released by the operator and taking into account the comfort of the user. The optimal power consumption strategy controls the switching of various loads that can be translated; on the other hand, on the premise of satisfying the temperature comfort, optimize the heat load at each time period. 3. According to the method of step 2 in claim 1, it is characterized in that the output model and the cost model of the established micro gas turbine. 4. According to the method of step 3 in claim 1, it is characterized in that the start-up time of the translatable load to be optimized is converted into a 0,1 vector representing its start-stop state, taking into account the comfort constraints of the user, so as to formulate the translatable load operation control strategy. 5. According to the method of step 3 in claim 1, it is characterized in that the PMV index is established to quantify the temperature comfort, and the space heat load and hot water load models are established with the temperature comfort as constraints. 6. The method according to step 4 in claim 1, characterized by constructing an operator revenue model and a user revenue model. 7. According to the method of step 4 in right 1, it is characterized in that the master-slave game model is solved: the master-slave game model can be divided into upper and lower sub-modules for solving; wherein the lower sub-module refers to the user, and the purpose is to ensure self-comfort Under the premise of constraints, the response set that responds to the strategy formulated by the operator is obtained; the upper layer game problem is solved by using the chaotic artificial fish swarm algorithm, and the lower layer model is solved by using YALMIP in MTALAB to obtain the strategy combination set of the operator and the user.