Comprehensive energy system based on network transmission loss and comfort demand and optimization method

Abstract

The invention relates to a comprehensive energy system based on network transmission loss and comfort requirements and an optimization method. The method comprises the following steps: S1, constructing an optimization model of a bilateral benefit game; step SA, establishing an upper-layer system optimization model; s1.1, establishing a dynamic network model of the natural gas system based on cooperation and complementation of a combined heat and power generation turbine, power-to-gas equipment and a power grid; s1.2, firstly, energy equipment constraints are determined; s1.3, determining a thermal dynamic pipe network constraint; s1.4, determining a lower-layer system optimization model; s2, simplifying and solving the model; s2, performing convexity, constraint relaxation and complex constraint reduction processing on the integrated energy system double-layer optimization operation model in the step S1; s2.1, carrying out model convexity and relaxation; s3, high-dimensional complex constraint simplification is carried out; and S3.1, reducing the natural gas dynamic network model.

Description

Technical Field

[0001] The present invention relates to the field of efficient energy saving and carbon reduction technology in green electricity consumption scenarios based on the dynamic characteristics of heterogeneous energy grids and thermal inertia characteristics of loads, and to an integrated energy system and optimization method based on grid transmission losses and comfort requirements. Background Art

[0002] Energy flows at different time scales through different transmission methods. Electricity, natural gas and heating network systems are planned and managed independently. Electricity and heat demand are the most important part of terminal energy consumption. As an effective measure to improve energy efficiency, the integrated energy system based on the combined heat and power (CHP) system is one of the main forms. However, the incoordination between high penetration of renewable energy and the widespread use of CHP units has become a challenge for the operation of the energy system. On the one hand, the fluctuation of wind power generation causes large changes in electricity prices during operation. When the wind power is large and the electricity price is low, the profit of CHP units will decrease. On the other hand, the electric power of CHP units is limited by the thermal power, and its thermal power must be targeted at the thermal energy demand of the load, resulting in large-scale "wind abandonment". Realizing the combined heat and power operation of CHP units is an effective way to improve the flexibility of the integrated energy system and promote the consumption of renewable energy.

[0003] The flexibility of cogeneration units will be conducive to the absorption of green electricity. Gas-fired cogeneration power plants generate electricity and heat at the same time. When the natural gas system supply is flexible enough to ensure the supply of natural gas, if the natural gas pipeline is interrupted, the gas-fired power generation system without dual-fuel capability may be restricted, which will eventually lead to transmission congestion problems in the power system. Natural gas pipeline interruption or pressure loss may limit the amount of gas transmission and cause power generation losses of generators, so it is extremely important to consider the dynamic operation safety of natural gas transmission. With the popularization of heating electrification (such as electrified heat pumps and electric boilers), electricity demand is increasingly affected by heating requirements. Thanks to the slow dynamic and high latency characteristics of the heating pipeline network, the thermal system can be equivalent to thermal energy storage, assisting the power system in peak load shaving and valley filling, and absorbing highly volatile and highly intermittent renewable energy.

[0004] The gradual liberalization of the distribution and sales sides has led to more and more private capital entering the sales sector to compete for users. As the number and types of optional energy suppliers increase, the status of users as the service subject has been significantly improved, and users have become the object of competition for service and occupation by energy suppliers.

[0005] Current research rarely discusses the optimization of the operation of integrated energy systems of large-scale power and natural gas systems and central heating systems. The inertia characteristics of each link of the system are rarely considered, and the optimization process does not consider the thermal comfort of the end-user demand response side and the heat loss of the thermal network during the transmission process. Considering the dynamic transmission loss characteristics of the thermal network, the traditional node method uses nonlinear functions and logical expressions to describe the propagation process and heat loss of the pipe temperature in the thermal system, which greatly increases the complexity of solving the electric-thermal coupling system.

[0006] In terms of the operation optimization of integrated energy systems, most of the research focuses on single-layer operation optimization problems. The traditional single-layer optimization model pays more attention to the economic cost of the overall energy system. The lower-level users completely passively accept the leadership of the upper level, and do not fully consider the interests and needs of the users themselves, and cannot reflect the initiative of the users. Energy suppliers need to change the traditional operation mode that only considers optimizing their own unit output and system safety and stability, and also consider the comfort and economic benefits of the users. In the research work of the two-layer integrated energy economic operation model, the gas and heat steady-state safety constraints are mainly used, and the inertia of the demand side and the response characteristics of the users are not fully considered, and the dynamic characteristics of the regional heating system and the dynamic user demand side characteristics are ignored. As a result, the operation strategy of the integrated energy system obtained may still not meet the supply and demand balance requirements on the grid and load side, and may even be completely out of control in extreme cases. Summary of the invention

[0007] The technical problem to be solved by the present invention is generally to provide an integrated energy system and optimization method based on network transmission loss and comfort requirements.

[0008] To solve the above problems, the technical solution adopted by the present invention is:

[0009] A comprehensive optimization method based on network transmission loss and comfort requirements includes the following steps:

[0010] Step S1, constructing an optimization model of bilateral interest game;

[0011] Step SA, establishing an upper system optimization model;

[0012] S1.1, a dynamic network model of the natural gas system was established based on the synergy and complementarity of cogeneration turbines, power-to-gas equipment and power grid;

[0013] S1.2, first, determine the energy device constraints;

[0014] S1.3, determine the thermal dynamic network constraints;

[0015] S1.4, determine the optimization model of the lower system;

[0016] S2, model simplification and solution; for the two-level optimization operation model of the integrated energy system in step S1, convexification, constraint relaxation, and complex constraint simplification are performed;

[0017] S2.1, convexification and relaxation of the model;

[0018] S3, simplification of high-dimensional complex constraints;

[0019] S3.1, Simplified natural gas dynamic network model;

[0020] S3.2, thermal dynamic network model simplification;

[0021] S3.3 Simplification of user-side temperature dynamic model;

[0022] S4, implements a solution strategy, uses KKT conditions to transform the original optimization problem into a planning problem with equilibrium constraints, uses the big M method to deal with complementary constraints, and solves the final single-level mixed integer linear programming problem.

[0023] In S2.1, first, the nonlinear energy consumption term of the coal-fired unit in the objective function is linearized as follows:

[0024]

[0025] Where: a i 、b i 、c i They are the secondary term coefficient, the primary term coefficient and the constant term coefficient of energy consumption of coal-fired units respectively;

[0026] Using the convex combination method, the interval Divide into N equal parts Assume that each segment point corresponds to a continuous variable Each line segment corresponds to a binary variable satisfy:

[0027]

[0028]

[0029] Where: 1 ≤β 1 , N ≤β N , i ≤β i +β i+1 ,

[0030] Where: x i is the i-th node in the segment interval, λ i is the continuous variable corresponding to the i-th node, For each line segment, there is a binary variable;

[0031] The consumption characteristics of the combined heat and power unit are quadratic equality constraints:

[0032]

[0033] Where: ch i，t (i=1, 2...6) represents the coefficients of the quadratic term, the linear term and the constant term of the consumption characteristics of the cogeneration unit;

[0034] Using quadratic convex relaxation to process the equality, we get the convex quadratic inequality constraint:

[0035]

[0036] Only when the optimal solution after relaxation satisfies the quadratic inequality, the equality sign holds and the optimization goal is to minimize the economic operation cost.

[0037] In step S4, the original two-layer optimization model of step S1 is simplified to the following form Max FU(x, y)

[0038] std(x, y)≥0, g(x, y)=0

[0039] Min FL(x,y)

[0040] stD(x, y)≥0, G(x, y)=0

[0041] For the lower layer problem, which is convex, the KKT condition can be replaced by

[0042] Max FU(x,y)

[0043] std(x, y)≥0, g(x, y)=0

[0044]

[0045] G(x, y) = 0

[0046]

[0047] An integrated energy system based on network transmission losses and comfort requirements executes the above method.

[0048] To sum up, in response to the problem of insufficient supply and demand flexibility caused by the increasing penetration rate of high proportion of renewable energy, the present invention relaxes the electricity-gas-heat multi-energy coupling system based on the actual characteristics of gas dynamics and thermal inertia on the grid-load side and the user demand response characteristics, fully considers the win-win bilateral economic interests of energy producers and users, and proposes an optimization method that meets the bilateral interests of energy producers and users.

[0049] In view of the huge differences in the transmission characteristics of electricity, gas grids, and heat, the upper layer established an optimization model with the goal of maximizing the total profit of energy producers, with the dynamic and safe operation of the gas system as the key constraint; the lower layer established a model that takes into account the optimal heating cost and thermal comfort of users, optimizes energy purchases using thermal inertia, expresses the range of heat demand through intervals, and considers the transmission loss of the heat network, the dynamic characteristics of building loads, the thermal comfort of the user side, and the economic benefits of the end user. Dynamically absorb renewable energy to meet the heat demand of users, thereby reducing the cost of heat demand. In addition, adding gas storage tanks reduces the overall production cost, and the transmission capacity of the pipeline is fully adjusted by storing and releasing gas, which improves the safety and flexibility of the operation of the integrated energy system. The present invention provides a more complete solution for efficient energy saving and carbon reduction in green electricity consumption scenarios that takes into account economy, flexibility, low carbon, and risk.

[0050] The present invention focuses on improving the flexibility of renewable energy access to multi-energy systems, aiming to meet the dynamic balance of the system and enhance safe and stable operation. By relaxing the constraints between the heterogeneous energy flows of electricity, heat, and gas, the thermal load management is promoted to change from a fixed rigid mode to a flexible and flexible mode, thereby helping the new power system to achieve decarbonization on the supply side and enhance controllability on the demand side. The present invention effectively improves the grid's ability to accept, configure, and regulate green electricity, and at the same time proposes a multi-energy system operation optimization method that takes into account the bilateral interests of households and businesses with network transmission losses and comfort requirements, ensuring that the multi-energy system achieves maximum efficient coordination on both the supply and demand sides while maintaining clean and low carbon, safe operation, and optimal economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 The schematic diagram of energy distribution of multiple energy flows in the integrated energy system is shown;

[0052] Figure 2 A schematic diagram of an equivalent model of heat distribution loss in thermal transmission is shown;

[0053] Figure 3 A schematic diagram of a natural gas dynamic system is shown;

[0054] Figure 4 A schematic diagram of the information interaction process of the double-layer operation optimization model is shown;

[0055] Figure 5 The schematic diagram of the solution process of the proposed optimization method is shown. DETAILED DESCRIPTION

[0056] like Figure 1-5 As shown, the integrated energy system and optimization method based on network transmission loss and comfort requirements of this embodiment include the following steps:

[0057] S1, construct an optimization model for bilateral interest game

[0058] In order to reflect the bilateral interest game between energy producers and users, Figure 1 , it is assumed that there is an independent system operator responsible for the coordinated and optimized operation of the entire integrated energy system.

[0059] The energy produced by energy producers comes from large wind farms, coal-fired units and natural gas produced by gas sources. The energy flow transmission pathways include regional power grids, natural gas pipelines, and energy conversion units such as combined heat and power (CHP) units and power-to-gas (PtG) equipment. CHP units obtain fuel from the gas grid and supply it to the power grid and heat network; PtG obtains electricity from the grid, produces natural gas and transmits it to the gas grid, realizing two-way transmission of natural gas and electricity. In order to better utilize the system flexibility, natural gas storage tank equipment is added to supplement or store the natural gas network balance. Distributed renewable energy, represented by wind power, is connected to the integrated energy system.

[0060] Energy users' electricity usage behavior is usually difficult to change, and most users are willing but unable to change their electricity usage behavior hour by hour in line with real-time electricity prices. Users seek to keep indoor temperatures within a reasonable range to meet their comfort level, while minimizing the energy consumption of user-side heating devices. CHP units are used as the heat source for centralized heating systems. When the heat supply is insufficient, electric boilers are quickly put into production to compensate for the heat supply, reflecting the coordinated and complementary characteristics of multiple energy flows.

[0061] like Figure 2 , where the heating network between the heat source and the dynamic load side can be equivalent to a thermal energy storage device. The heating power does not have to track the changes in thermal load in real time. The excess heat can be stored in the heating network or obtained from the heating network. The heat input of the thermal system is relatively flexible and does not need to strictly meet the real-time balance relationship between the heating end and the heat use side.

[0062] 1 Upper-level system optimization model

[0063] In order to reflect the synergistic and complementary characteristics of multi-energy supply, the fast-responding cogeneration turbine and power-to-gas equipment are used to coordinate and complement the power grid. The dynamic network model of the natural gas system is considered and established in the upper-level optimization problem. The goal is to maximize the total profit of energy producers, and the safe and stable operation of the power and natural gas systems is the constraint condition. The reliability, sustainability and economy of the natural gas supply need to be guaranteed. The optimization objective function is described as:

[0064]

[0065] Where: ST represents the set of conventional coal-fired units, GT represents the set of gas-fired CHP units, GF represents the set of natural gas fields, represents the price of electricity at time t, represents the price of heat at time t, represents the profit of gas field i at time t, represents the cost function of conventional coal-fired unit i, represents the cost function of gas-fired CHP unit i;

[0066] Energy device constraints

[0067] ① Power output constraints of coal-fired units

[0068] u i，t P i，min ≤P i，t ≤u i，t P i，max

[0069] Where: P i，min and P i，max is the minimum and maximum power output of coal-fired unit i, u i，t It is a 0-1 integer variable indicating the operating status of coal-fired unit i.

[0070] ②Back pressure cogeneration unit is a special operation mode of extraction cogeneration unit, and its characteristics are described as follows:

[0071]

[0072] Where: Indicates the maximum thermal output of unit i, and p i,t Indicates the maximum / minimum power output of unit i, S 1，i and S 2，i It is expressed as the electric-to-heat ratio of two working conditions, that is, the decrease in electric output when the unit heat output increases, S mx，i It is expressed as the electric-to-heat ratio under the back-pressure condition of unit i, that is, when all the inlet steam is used for extraction heating, the heat output is maximum and the electric output is minimum, it corresponds to the minimum condensing condition, which is similar to the back-pressure condition; and They represent the upward climbing rate and downward climbing rate of unit i respectively; sdi is a constant, which is expressed as the corresponding electric output value when the thermal output on the back pressure curve is zero.

[0073] ③Climbing power of coal-fired units

[0074]

[0075] Where: rdown i andrup i They represent the upward climbing rate and downward climbing rate of coal-fired unit i respectively.

[0076] ④Power system spinning reserve constraints

[0077]

[0078] Where: SR up and SR down It is the maximum / minimum rotation reserve constraint.

[0079] ⑤ Wind turbine power output constraints

[0080]

[0081] Where: and P w，t is the predicted and actual electric power output of the wind farm w.

[0082] ⑥ Active power balance constraints of multi-energy systems

[0083]

[0084] ⑦Power system flow constraints

[0085]

[0086] Where NP2G represents the collection of power-to-gas plants, represents the electric energy consumed by power-to-gas plant i during period t, BP i，t Represents the electric power consumed by electric boiler i during time period t. and They represent the coal-fired pure condensing units, gas-fired CHP units and wind farms connected to node n, respectively. n，t Represents the electrical load at node n at time t.

[0087] The natural gas dynamic system structure adopted by the present invention is composed of Figure 3 As shown, it includes energy production and supply (natural gas fields or PtG plants), network transmission pipelines (trunk networks), natural gas storage and supply tanks, and comprehensive user consumption (power generation users, gas users, etc.). It is assumed that natural gas supply can come from natural gas fields, P2G plants, and gas storage tanks.

[0088] ① Constraints on natural gas field production

[0089]

[0090] Where: and represents the minimum and maximum gas production of natural gas s, Indicates natural gas

[0091] s is the gas production in time period t. NGF is the collection of natural gas fields.

[0092] ②The electrical process conversion efficiency of PtG equipment is

[0093]

[0094] Where: represents the gas production of P2G plant s in period t, α PtG It represents the energy conversion efficiency of electricity to gas, and LHV represents the lower calorific value of natural gas.

[0095] ③Pipeline momentum equation constraints

[0096]

[0097] Where: α is the angle between the pipeline axis and the ground plane; g is the gravitational acceleration at that location; d is the pipeline diameter; λ is the friction coefficient of the inner wall of the pipeline.

[0098] ④Pipeline material equation constraints

[0099]

[0100] Where: ρ represents gas density; v is gas velocity; x represents the position of the gas in the pipeline; t represents time.

[0101] ⑤ Constraints of pipeline energy conservation equation

[0102]

[0103] In the formula: Ω is the heat transfer rate; e represents the internal energy per unit gas; h is the specific entropy, that is, the entropy of 1 kg of working fluid, and the working fluid is the medium for realizing the conversion of internal energy and mechanical energy.

[0104] ⑥ Natural gas storage tanks compensate for the imbalance between the supply side and the demand side. The storage and release of gas storage tanks are described as

[0105]

[0106] In the formula, GS s，t represents the gas storage capacity of gas tank s in time period t; GS s and Indicates the minimum and maximum gas storage capacity of the gas tank; f s，t is a 0-1 integer variable of the gas tank at time t. s，t =1, indicating that the gas tank is in the state of inflation. s，t =0, it means the gas tank is in the deflated state; and Indicates the maximum filling and deflation rate of the air tank.

[0107] ⑦ According to the type of natural gas node load, it is divided into two categories: civil gas load and gas turbine power generation load.

[0108]

[0109] Where: GL m，t is the total load at gas node m at time t, is the civil gas load value, is the gas turbine gas load value.

[0110] ⑧ Civil gas load is expressed as gas outflow at relevant nodes, which is restricted

[0111] Where: and The minimum and maximum limit for civil gas load.

[0112] ⑨ In the natural gas pipeline network, the gas-fired CHP unit is a gas load, and its gas consumption depends on the power and heat demand; in the power grid, the gas-fired CHP unit is a power source controlled by real-time optimization scheduling. As the load consumption end of the natural gas node, the gas-fired CHP unit has a gas consumption characteristic described as

[0113]

[0114] Where: represents the equivalent power generation of gas unit i in period t, TR is the heat-to-electricity factor of gas-fired CHP unit i, g 1，i ~g 3，i represents the gas consumption coefficient of gas generator set i, and ND represents the set of gas network nodes. GT (m) represents the set of gas-fired CHP units connected to the natural gas pipeline node m. ⑩ According to the law of conservation of energy, the gas inflow and outflow at each node are equal.

[0115]

[0116] Where: ND represents the gas network node set, GC(m) represents the gas network node set connected to node m, NGL L Represents the natural gas residential load node set, NGL D represents the gas turbine load node set, and SC(m) represents the gas storage tank set connected to node m. represents the association matrix between natural gas fields and natural gas pipeline networks, and represents the connection relationship between gas field s and node m. When , it means that the two are not connected, otherwise they are connected; the matrix represents the association matrix between the P2G plant and the natural gas pipeline network, It means that the two are not connected, otherwise they are connected; is the association matrix between civil gas load and gas node, indicating whether there is a residential gas load connected to node m. There is a connection when , and not when it is not; is the association matrix between the gas turbine and the load gas node m, indicating whether there is a gas power generation load connected to the node m. There is a connection when , otherwise there is no connection; f mn，t is the amount of gas flowing from node m to the outlet at time t.

[0117] Thermal dynamic network constraints

[0118] In order to improve the solution efficiency, this paper adopts a thermal transmission model based on concentrated linear heat network loss. The heat loss of the heating system mainly depends on two factors: the heating temperature and the geographical range of the heating system. The heat network transmission loss mainly comes from external heat conduction, which is approximately estimated by the temperature distribution parameter expression.

[0119] ① The partial differential equation for the temperature variation in the heat pipe network over time and space is described as

[0120]

[0121] Where: T g is the ambient temperature; k is the heat transfer coefficient of the pipeline; A is the cross-sectional area of ​​the pipeline; T x，t is the pipe temperature at time t at a distance x from the beginning of the pipe; m is the microelement Δx water flow; C p is the specific heat capacity.

[0122] ② Heat network transmission loss: The mapping relationship between the inlet and outlet temperatures of the water supply and return networks is simplified as follows:

[0123]

[0124] Where: and They represent the inlet temperature and outlet temperature of the water supply network pipe p at time t respectively; and They represent the inlet temperature and outlet temperature of the water supply network pipe p at time t respectively; τ p，t Characterizes the transmission delay of pipeline p at time t.

[0125] ③The heat loss of the heating system is calculated by accumulating the heat loss of each pipe in the water supply network and the return water network. Each heating pipe is equivalent to two virtual heat storage tanks, and the internal temperatures of the water supply and return water networks are respectively expressed as and According to the temperature difference between the inside and outside of the heat storage tank, the overall heat loss is calculated as

[0126]

[0127] Where: and They represent the external heat loss power of pipe p in the water supply and return networks respectively; represents the soil temperature around the pipeline at time t; k p is the equivalent heat transfer coefficient of pipe p; L p Indicates the length of pipeline p.

[0128] 2. Lower-level system optimization model

[0129] From the electricity consumption behavior of user terminals, we can see that most users are willing but unable to change their electricity consumption behavior hour by hour according to the real-time electricity price. Changing the electricity consumption behavior of users will bring great discomfort. However, thermal balance is different from power balance. The supply and demand of thermal power does not need to ensure real-time balance, and it will not bring great discomfort to users.

[0130] In order to meet the thermal demand on the user side, the demand response of the power load is not considered. It is assumed that the load caused by non-electric heat conversion equipment is a rigid load. The main consideration is that users use the thermal inertia of buildings to meet their thermal needs. Users can purchase thermal energy from upper-level energy producers according to energy prices to meet their own needs.

[0131] The lower optimization problem is to meet the thermal demand range under the premise of ensuring the thermal comfort of users through the building thermodynamic dynamic model, with the minimum user's heat purchase cost as the optimization goal, which not only controls the indoor temperature within a reasonable range, but also minimizes the user's heat purchase cost. The optimization objective function is described as:

[0132]

[0133] Where: τ opt Expressed as the most comfortable temperature, to increase the convexity of the function, increase the penalty factor σ τ , It is determined according to the user's requirements for thermal comfort.

[0134] ① Electric boiler operation constraints

[0135] When the heat supply is insufficient, the electric boiler is quickly put into operation to compensate for the heat supply, as described below:

[0136]

[0137] Where: I EB Indicates electric boiler set, BH i，t represents the heat output of electric boiler i at time t, Indicates the maximum thermal output of electric boiler i, BP i，t represents the electric power consumed by electric boiler i at time t, It represents the electric-to-heat conversion efficiency of electric boiler i.

[0138] ② Building temperature dynamic model

[0139] Partial differential equations are used to describe the dynamic relationship between the internal temperature of the building and the thermal parameters of the building, the input heating / cooling power, and the external ambient temperature, taking into account the heat dissipation by ventilation and the heat transfer process through the building structure.

[0140] Then the changes of indoor air temperature and building structure temperature of the building are described as

[0141]

[0142] Setting the upper and lower limits of the indoor temperature can be described as

[0143]

[0144] Where: and Respectively represent the internal temperature of the building and the temperature of the building wall structure; and They represent the building's internal environment and wall structure heat capacity parameters respectively; and They represent the building ventilation, indoor-to-wall and wall-to-outdoor heat transfer parameters respectively; is the initial setting value of indoor temperature; σ OB The allowable fluctuation range of indoor temperature. Indicates the heating load of indoor space in a building; Indicates the cooling load of indoor space in a building.

[0145] ③The heat source provides heat to meet the indoor temperature

[0146]

[0147] ④ Using the building's heat storage capacity, the indoor temperature is allowed to vary within the range of

[0148]

[0149] Where: and They represent the minimum and maximum indoor temperature respectively, and Δτ represents the maximum temperature change in adjacent time intervals.

[0150] ⑥ To ensure that the indoor temperature in the next period is higher than the minimum value, the following conditions must be met

[0151]

[0152] After discretizing the dynamic model of building load temperature, we get

[0153]

[0154] Where: Let w = R h Q t . represents the indoor temperature during period t, represents the outdoor temperature during period t, R h represents the thermal resistance of the building envelope, and Δt represents the time interval between adjacent scheduling.

[0155] Enter the total heat power of the building Q t for

[0156]

[0157] To ensure that the indoor temperature in the next period is higher than the minimum value, that is, Then input the total thermal power of the building Q t for

[0158]

[0159] In summary, the total thermal power Q t The adjustment range should meet

[0160] S2. Model simplification and solution

[0161] For the two-layer optimization operation model of the integrated energy system established in the present invention, in order to balance its technical goals of robustness, strategy optimality and computational efficiency and quickly obtain the optimal decision, it is necessary to complete convexification, constraint relaxation, complex constraint simplification and other processing.

[0162] 1. Model convexification and relaxation

[0163] ① The nonlinear energy consumption term of the coal-fired unit in the objective function is linearized as follows:

[0164]

[0165] Where: a i 、b i 、c i They are respectively the quadratic coefficient, linear coefficient and constant coefficient of energy consumption of coal-fired units.

[0166] Using the convex combination method, the interval Divide into N equal parts Assume that each segment point corresponds to a continuous variable Each line segment corresponds to a binary variable satisfy:

[0167]

[0168] Where:1 ≤β 1 , N ≤β N , i ≤β i +β i+1 , Where: x i is the i-th node in the segment interval, λ i is the continuous variable corresponding to the i-th node, There is a binary variable corresponding to each line segment.

[0169] The combined heat and power unit is the load consumption of the natural gas system and is supplied by the source side of the natural gas system (gas source or gas storage device). It can be seen that the consumption characteristic is a quadratic equation constraint:

[0170]

[0171] Where: ch i，t (i=1, 2...6) represents the coefficients of the quadratic term, the linear term and the constant term of the consumption characteristics of the cogeneration unit;

[0172] Using quadratic convex relaxation to process the equality, we get the convex quadratic inequality constraint:

[0173]

[0174] If and only if the optimal solution after relaxation satisfies the quadratic inequality equality, the optimization goal is to minimize the economic operation cost, including the gas source consumption cost. In order to minimize the economic cost of the natural gas system, the quadratic inequality constraint is an effective constraint. Therefore, it can be proved that the quadratic convex relaxation method used in the present invention is accurate.

[0175] 2. Simplification of high-dimensional complex constraints

[0176] (1) Simplification of natural gas dynamic network model

[0177] From the perspective of mathematical model, the state of energy flow in the gas grid is described by partial differential equations. The entire optimization problem is a mixed optimization problem containing partial differential equations and algebraic equation constraints, which cannot be solved directly and requires discretization and simplification of partial differential equations.

[0178] In order to simplify the relationship model between the dynamic flow rate and pressure of natural gas, combined with the actual application of natural gas, the following simplification conditions are introduced. Simplification 1: Simplify the energy conservation equation. The gas has sufficient time to achieve temperature equilibrium with the outside world. Under isothermal conditions, the balance equation written by energy conservation is equivalent to the balance equation written by momentum conservation, so the pipeline energy conservation equation can be ignored.

[0179] Simplification 2: Use average value instead of variable value. Use pipeline average compression ratio z avgand the average temperature T avg Substitution principle in the gas state equation (p = ρ·z·R a ·T) Gas compression ratio z and temperature T are

[0180] Simplification 3: Simplification of high-order terms. From the definition of the speed of sound, we can know that its essence is the propagation speed of tiny pressure disturbances in the medium.

[0181]

[0182] Combining simplification 2 and simplification 3, we get under isothermal conditions, which is

[0183] Assuming that the axis of the natural gas pipeline is parallel to the ground plane and ignoring the first term of the momentum balance equation, we can get

[0184]

[0185] The material gas flow rate f is used to represent the flow of natural gas in the pipeline. It is related to the natural gas density ρ, flow velocity v and pipeline cross-sectional area A. The pipeline gas dynamic equation is simplified under isothermal conditions to obtain

[0186]

[0187] Set the initial conditions and let the pressure at the first end be p 1 , the end pressure is p 2 ,have to in: Therefore, the gas dynamic model is equivalent to the steady-state gas model, which can prove the correctness and rationality of the gas dynamic model of the present invention.

[0188] The second-order conservative LW difference method is used to discretize the gas dynamic model, and we get

[0189]

[0190] Where: for each pipeline ij, Δx is equal to the pipeline length Lij, the pipeline node subscript i+1 corresponds to the pipeline end node j, MFij and MEij represent the natural gas material flow rate at the front end and the end of pipeline ij, respectively.

[0191] (2) Simplification of thermal dynamic network model

[0192] Using the finite difference method, the pipe temperature T x，t Expanding time and space, we get

[0193]

[0194] The difference format is described as follows:

[0195]

[0196] The temperature variation of any point in the pipe over time can be described as

[0197]

[0198] Where: Δx is the distance step of the thermal network in the difference method, and Δt is the time step of the thermal network in the difference method.

[0199] Combined with the temperature distribution in the water supply network of the thermal system, its dynamic process can be described as

[0200]

[0201] Where: represents the average heating temperature of the pipe (k, l) at time t. This formula shows that the water temperature of the pipe (k, l) depends on the heat input and external heat dissipation.

[0202] The temperature change process in the return water network can be described as

[0203]

[0204] Where: represents the average reheating temperature of pipe (k, l) at time t.

[0205] (3) Simplification of user-side temperature dynamic model

[0206] The discrete difference describing the dynamic temperature of the building is:

[0207]

[0208] Where: and They represent the internal temperature of the building and the wall structure temperature of the building at time t respectively; and They represent the heat capacity parameters of the building’s internal environment and wall structure respectively; and They represent the building ventilation, indoor-to-wall and wall-to-outdoor heat transfer parameters respectively; is the initial setting value of indoor temperature; σ o B is the allowable fluctuation range of indoor temperature. represents the heating load on the indoor space of the building at time t; Represents the cooling load on the indoor space of the building at time t.

[0209] 3. Solution strategy

[0210] The present invention utilizes KKT conditions to transform the original optimization problem into a programming problem with equilibrium constraints, utilizes the big M method to process the complementary constraints, and solves the resulting single-layer mixed integer linear programming problem.

[0211] The original two-level optimization model is simplified as follows

[0212] Max FU(x,y)

[0213] std(x, y)≥0, g(x, y)=0

[0214] Min FL(x,y)

[0215] stD(x, y)≥0, G(x, y)=0

[0216] For the lower layer problem, which is convex, the KKT condition can be replaced by

[0217] Max FU(x,y)

[0218] std(x, y)≥0, g(x, y)=0

[0219]

[0220] G(x, y) = 0

[0221]

[0222] The present invention is fully described for a clearer disclosure, and the prior art is not listed one by one.

[0223] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some of the technical features therein with equivalents; it is obvious for those skilled in the art to combine multiple technical solutions of the present invention. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A comprehensive optimization method based on network transmission loss and comfort requirements, characterized by: The method comprises the following steps: Step S1, constructing an optimization model of bilateral interest game; Step SA, establishing an upper system optimization model; S1.1, a dynamic network model of the natural gas system was established based on the synergy and complementarity of cogeneration turbines, power-to-gas equipment and power grid; S1.2, first, determine the energy device constraints; S1.3, determine the thermal dynamic network constraints; S1.4, determine the optimization model of the lower system; S2, model simplification and solution; for the two-level optimization operation model of the integrated energy system in step S1, convexification, constraint relaxation, and complex constraint simplification are performed; S2.1, convexification and relaxation of the model; S3, simplification of high-dimensional complex constraints; S3.1, Simplified natural gas dynamic network model; S3.2, thermal dynamic network model simplification; S3.3 Simplification of user-side temperature dynamic model; S4, implements a solution strategy, uses KKT conditions to transform the original optimization problem into a planning problem with equilibrium constraints, uses the big M method to deal with complementary constraints, and solves the final single-level mixed integer linear programming problem.

2. The comprehensive energy optimization method based on network transmission loss and comfort requirements according to claim 1 is characterized in that: In step S1, it is assumed that there is an independent system operator responsible for the coordinated and optimized operation of the entire integrated energy system; with the help of the system; the system includes energy producers, power systems, thermal energy systems and gas energy systems; Energy producers produce energy from wind farms and power grids, heat from combined heat and power (CHP) units, and natural gas from gas sources; The wind farm and the power grid are connected to the electrical load through the regional grid of the power system; Thermal energy system, including gas turbine and CHP unit connected to gas turbine, thermal system and boiler; thermal load connected to thermal system; The gas source supplies gas to the gas turbine and gas energy system through the natural gas pipeline network; the gas energy system is connected to the gas boiler to provide heat energy to the heat load; The gas source pipeline is connected to the natural gas storage tank and the comprehensive user consumption side; The CHP unit of combined heat and power adopts Kalina cycle; The comprehensive user consumption side includes power generation users and gas users.

3. The comprehensive optimization method based on network transmission loss and comfort requirements according to claim 1 is characterized in that: For the S1.1 natural gas system dynamic network model, with the safe and stable operation of the power and natural gas systems as constraints, the optimization objective function is established as follows: Where: ST represents the set of coal-fired units, GT represents the set of units, GF represents the set of natural gas fields, represents the price of electricity at time t, represents the price of heat at time t, represents the profit of gas field i at time t, represents the cost function of conventional coal-fired unit i, represents the cost function of gas-fired CHP unit i; In S1.2, in determining the energy equipment constraints, the power output constraints of the coal-fired gas turbine units u i,t Q i,min ≤P i,t ≤u i,t Q i,max (2); Where: P i,min and P i,max is the minimum and maximum power output of coal-fired unit i, u i,t is a 0-1 integer variable indicating the operating status of coal-fired unit i; Then, the combined heat and power unit adopts a back pressure combined heat and power unit with the following characteristics: Where: Indicates the maximum thermal output of cogeneration unit i, and p i,t Respectively represent the maximum and minimum power output of unit i, S 1,i and S 2,i They are respectively expressed as the electric-to-heat ratio of the two working conditions, i.e., the reduction in electric output corresponding to the increase in unit heat output; S max,i It is expressed as the electric-to-heat ratio under the back-pressure condition of unit i, that is, when all the inlet steam is used for extraction heating, the heat output is maximum and the electric output is minimum, it corresponds to the minimum condensing condition, which is similar to the back-pressure condition; and They represent the upward climbing rate and downward climbing rate of unit i respectively; sd i is a constant, which is expressed as the electric output value corresponding to the zero thermal output on the back pressure curve of the cogeneration unit; Secondly, determine the ramping power of coal-fired units; Where: rdown i andrup i They represent the upward climbing rate and downward climbing rate of coal-fired unit i respectively; Again, power system spinning reserve constraints; Where: SR up and SR down They are expressed as maximum and minimum rotation reserve constraints respectively; Afterwards, the power output constraints of the wind turbines are determined; Where: and P w,t The predicted and actual electric power output of the wind farm w; Later, the active power balance constraints of the multi-energy system are determined; Then, determine the power system flow constraints; Where NP2G represents the collection of power-to-gas plants, represents the electric energy consumed by power-to-gas plant i during period t, BP i,t represents the electric power consumed by electric boiler i in time period t; and They represent the coal-fired pure condensing units, gas-fired CHP units and wind farms connected to node n, respectively. n,t Represents the electrical load at node n at time t.

4. The comprehensive optimization method based on network transmission loss and comfort requirements according to claim 3 is characterized in that: In S1.3, first, the heat loss of the heating system depends on the heating temperature and the geographical range of the heating system; the heat network transmission loss comes from external heat conduction and is approximately estimated by the temperature distribution parameter expression; The partial differential equation for the temperature in the thermal network pipes changing with time and space is: Where: T g is the ambient temperature; k is the heat transfer coefficient of the pipeline; A is the cross-sectional area of ​​the pipeline; T x,t is the pipe temperature at time t at a distance x from the beginning of the pipe; m is the microelement Δx water flow; C p is the specific heat capacity; Then, the heat network transmission loss: the mapping relationship between the inlet and outlet temperatures of the water supply and return networks is simplified as Where: and They represent the inlet temperature and outlet temperature of the water supply network pipe p at time t respectively; and They represent the inlet temperature and outlet temperature of the water supply network pipe p at time t respectively; τ p,t Characterizes the transmission delay of pipeline p at time t; Secondly, the heat loss of the heating system is calculated by accumulating the heat loss of each pipe in the water supply network and the return network; the internal temperatures of the water supply and return networks are expressed as and Estimate; Based on the temperature difference between the inside and outside of the heat storage tank, the overall heat loss is calculated as Where: and They represent the external heat loss power of pipe p in the water supply and return networks respectively; represents the soil temperature around the pipeline at time t; k p is the equivalent heat transfer coefficient of pipe p; L p Indicates the length of pipeline p.

5. The comprehensive optimization method based on network transmission loss and comfort requirements according to claim 3 is characterized in that: In step S1.4, S1.4.1, first, it is assumed that the loads caused by non-electric heat conversion equipment are rigid loads; The lower optimization problem satisfies the thermal demand range through the building thermodynamic dynamic model, takes the minimum user's heat purchase cost as the optimization goal, and controls the indoor temperature within the set range. The optimization objective function is described as: Where: τ opt Expressed as the most comfortable temperature, adding penalty factor σ τ , Determined according to the user's requirements for thermal comfort; Secondly, determine the operation constraints of the electric boiler; when the heat supply is lower than the set threshold, the electric boiler starts to provide compensation heat, as described below: Where: I EB Indicates electric boiler set, BH i,t represents the heat output of electric boiler i at time t, Indicates the maximum thermal output of electric boiler i, BP i,t represents the electric power consumed by electric boiler i at time t, represents the electric-to-heat conversion efficiency of electric boiler i; S1.4.2, determine the dynamic model of terminal building temperature as electrical load and thermal load; Firstly, partial differential equations are used to describe the dynamic relationship between the internal temperature of the building and the thermal parameters of the building, the input heating / cooling power, and the external ambient temperature, taking into account the heat dissipation by ventilation and the heat transfer process through the building structure. Then the changes of indoor air temperature and building structure temperature of the building are described as Set the upper and lower limits of the indoor temperature. Where: and Respectively represent the internal temperature of the building and the temperature of the building wall structure; and They represent the building's internal environment and wall structure heat capacity parameters respectively; and They represent the building ventilation, heat conduction parameters between indoor and wall, and between wall and outdoor respectively; is the initial setting value of indoor temperature; σ OB The allowable fluctuation range of indoor temperature; Indicates the heating load of indoor space in a building; Indicates the cooling load of indoor space in a building; The heat source provides heat to meet the indoor temperature Using the building's thermal storage capacity, the indoor temperature can be varied within a set range. Where: and They represent the minimum and maximum indoor temperatures respectively, and Δτ represents the maximum temperature change in adjacent time intervals; Again, to ensure that the indoor temperature is higher than the minimum value in the next period, the following conditions must be met The building load temperature dynamic model is discretized to obtain Where: Let w = R h Q t ; represents the indoor temperature during period t, represents the outdoor temperature during period t, R h represents the thermal resistance of the building envelope, Δt represents the time interval between adjacent scheduling; Enter the total heat power of the building Q t for To ensure Then input the total thermal power of the building Q t for Total thermal power Q t The adjustment range should meet 6. The comprehensive optimization method based on network transmission loss and comfort requirements according to claim 3 is characterized in that: In S2.1, first, the nonlinear energy consumption term of the coal-fired unit in the objective function is linearized as follows: Where: a i 、b i 、c i They are the secondary term coefficient, the primary term coefficient and the constant term coefficient of energy consumption of coal-fired units respectively; Using the convex combination method, the interval Divide into N equal parts Assume that each segment point corresponds to a continuous variable Each line segment corresponds to a binary variable satisfy: where: λ1 ≤ β1, λ N ≤ β N 、λ i ≤ β i + β i+1 、 Where: x i is the i-th node in the segment interval, λ i is the continuous variable corresponding to the i-th node, For each line segment, there is a binary variable; The consumption characteristics of the combined heat and power unit are quadratic equality constraints: Where: ch i,t (i=1,2…6) represents the coefficients of the quadratic term, linear term and constant term of the consumption characteristics of the cogeneration unit; Using quadratic convex relaxation to process the equality, we get the convex quadratic inequality constraint: Only when the optimal solution after relaxation satisfies the quadratic inequality, the equality sign holds and the optimization goal is to minimize the economic operation cost.

7. The comprehensive optimization method based on network transmission loss and comfort requirements according to claim 3 is characterized in that: In S3.1, first, the state of energy flow in the gas grid is described by partial differential equations, and the following simplified conditions are introduced; Then, simplify the energy conservation equation and ignore the pipeline energy conservation equation; Use the average value instead of the change value, and use the average compression ratio Z of the pipeline avg and the average temperature T avg Substituting the ideal gas state equation (p = ρ·z·R a ·T) in which the gas compression ratio z and temperature T are Where: p represents the gas density, p is the gas pressure; Simplify high-order terms; obtain the propagation speed of small pressure disturbances in the medium through the definition of sound speed; Secondly, by simplifying formulas (36) and (37), we can obtain under isothermal conditions: Again, assuming that the axis of the natural gas pipeline is parallel to the ground plane and ignoring the first term of the momentum balance equation, we can get The material gas flow rate f is used to represent the flow of natural gas in the pipeline. It is related to the natural gas density ρ, flow velocity v and pipeline cross-sectional area A. The pipeline gas dynamic equation is simplified under isothermal conditions to obtain After that, the initial conditions are set so that the pressure at the first end is p1 and the pressure at the end is p2, and we get in: Where: d is the pipe diameter; λ is the friction coefficient of the inner wall of the pipe; Then, the second-order conservative LW difference method is used to discretize the gas dynamic model, and we get Where: for each pipeline ij, Δx is equal to the pipeline length Lij, the pipeline node subscript i+1 corresponds to the pipeline end node j, MFij and MFij represent the natural gas material flow rate at the front end and the natural gas material flow rate at the end of pipeline ij respectively; In S3.2, first, the finite difference method is used to convert the pipe temperature T x,t Expanding time and space, we get The difference format is described as follows: The temperature variation of any point in the pipe over time can be described as Where: Δx is the distance step of the thermal network in the difference method, Δt is the time step of the thermal network in the difference method; Then, combined with the temperature distribution in the water supply network of the thermal system, the dynamic process is described as Where: represents the average heating temperature of pipe (k, l) at time t. Formula (46) shows that the water temperature of pipe (k, l) depends on the heat input and external heat dissipation; Secondly, the temperature change process in the return network is described as Where: represents the average reheating temperature of the pipe (k, l) at time t; In S3.3, the dynamic discrete difference of the building temperature is Where: and They represent the internal temperature of the building and the wall structure temperature of the building at time t respectively; and They represent the heat capacity parameters of the building’s internal environment and wall structure respectively; and They represent the building ventilation, indoor-to-wall and wall-to-outdoor heat transfer parameters respectively; is the initial setting value of indoor temperature; σ OB The allowable fluctuation range of indoor temperature; represents the heating load on the indoor space of the building at time t; Represents the cooling load on the indoor space of the building at time t.

8. The comprehensive optimization method based on network transmission loss and comfort requirements according to claim 3 is characterized in that: In step S4, the original two-layer optimization model of step S1 is simplified as follows: Max FU(x,y) std(x,y)≥0,g(x,y)=0 Min FL(x,y) stD(x,y)≥0,G(x,y)=0 For the lower layer problem, which is convex, the KKT condition can be replaced by 9. An integrated energy system based on network transmission loss and comfort requirements, characterized by: Execute the method of claim 1.

Images