An optimal scheduling method for regional electric heating systems considering the flexibility constraints of electric heating equipment and heating network characteristics

Abstract

The present invention discloses a method for optimizing and scheduling a regional electric and thermal system taking into account the flexibility constraints of electric and thermal equipment and the characteristics of a thermal network, and belongs to the field of integrated energy system operation optimization. The method is as follows: a multi-energy equipment power model and an electric and thermal network model are constructed, based on the equipment regulation characteristics, considering the uncertainty on both the source and load sides, and constructing the electric and thermal flexibility supply and demand constraints; considering the time correlation of the flexibility constraints and the thermal inertia of the thermal network, an economic operation model is constructed with the goal of minimizing the sum of the external energy purchase cost, the system operation cost and the penalty for abandoned light. This method takes into account the time correlation of multi-energy flexibility and the thermal inertia of the thermal network, improves the system precision and the accuracy of the model simulation results, provides conditions for dealing with the volatility of electric and thermal loads and the uncertainty of new energy, and also provides a reference for further studying the role of thermoelectric coupling characteristics in the flexibility of power system optimization scheduling, which is in line with the current development trend of the power grid from single electric energy to multi-energy coupling.

Description

Technical Field

[0001] The present invention relates to a method for optimizing and dispatching a regional electric heating system taking into account the flexibility constraints of electric heating equipment and the characteristics of a heating network, and belongs to the field of integrated energy system operation optimization. Background Art

[0002] The rapid growth in energy demand poses significant environmental challenges. To achieve carbon neutrality, countries around the world have introduced a series of policies to control carbon emissions. One effective approach is to vigorously develop renewable energy while promoting the synergy and complementarity of multiple energy sources. District electric heating systems utilize distributed generation, renewable energy, and energy storage technologies to promote the interaction between supply and demand of multiple energy sources, thereby improving regional energy efficiency and renewable energy consumption.

[0003] With the continuous increase in installed renewable energy capacity, renewable energy sources such as wind and solar are increasingly being developed and utilized. However, the significant fluctuations and uncertainties in power output and user load associated with a high proportion of renewable energy pose significant challenges to the operation of regional thermal power systems. This paper addresses this issue by providing a method for optimizing the scheduling of regional thermal power systems that considers the flexibility constraints of thermal power equipment and the characteristics of the thermal network. Summary of the Invention

[0004] In order to solve the problems in the background technology, the present invention provides a method for optimizing the scheduling of regional electric heating systems taking into account the flexibility constraints of electric heating equipment and the characteristics of the heating network. This method provides conditions for coping with the volatility of electric heating loads and the uncertainty of new energy sources, and has certain guiding significance for optimizing the scheduling of regional electric heating systems, improving the system operation safety margin, and achieving safe and economical operation.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] First, based on the working principle and characteristics of multi-energy equipment, a multi-energy equipment power model is constructed to determine the thermal-to-electricity output ratio, rated power and other parameters of components such as cogeneration units, gas boilers, distributed gas units, and batteries; considering the equipment regulation characteristics, flexibility supply constraints are established; secondly, considering the thermal inertia of the heating network, an electric heating network model is established, and the electrical and thermal power balance equations are written; based on historical data, the probability intervals of distributed photovoltaic and electric heating loads are constructed to obtain flexibility demand constraints; an optimization model is established with the minimum sum of external energy purchase costs, system operating costs, and curtailment penalties as the objective function. The model includes flexibility supply constraints and flexibility demand constraints, and the interior point method is used to solve the optimization model to obtain the optimal operation and scheduling plan for the regional electric heating system.

[0007] (1) Modeling methods for multi-energy equipment power model and electric heating network model

[0008] By analyzing the physical structure of important multi-energy equipment such as cogeneration units, gas boilers, distributed gas units, and batteries, and considering the operating characteristics of different types of thermoelectric coupling elements, an electric and thermal operation characteristic model of the cogeneration unit, an operation model of the gas boiler, an operation model of the distributed gas unit, and a battery characteristic model were established. An electric and thermal network model was established considering the thermal inertia of the heating network, laying the foundation for the subsequent construction of flexibility constraints and the calculation of the optimal economic operation and scheduling of the electric and thermal system.

[0009] (2) Construction method of source-load two-sided uncertainty model

[0010] Based on historical data collection and literature research, the Beta distribution function is used to describe the randomness and volatility of distributed photovoltaic output, and the normal distribution function is used to describe the randomness and volatility of electric and thermal load. Based on the uncertainty of both the source and the load, the probability intervals of photovoltaic output and electric and thermal load are constructed.

[0011] (3) Optimal economic operation scheduling scheme considering flexibility constraints

[0012] Based on the aforementioned multi-energy device power model and electric heating network model, a flexibility supply constraint was constructed. Based on the collection of historical PV output data and literature research, the historical distributed PV output and electric heating load data curves were fitted, and the probability distribution function parameters of distributed PV output and electric heating load were determined to construct a flexibility demand constraint. An optimization model with the objective function of minimizing the sum of external energy purchase costs, system operating costs, and curtailment penalties was developed, and solved using the interior point method to obtain the optimal economic operation and dispatch solution.

[0013] In the above technical solution, further, the economic operation model aiming to minimize the sum of external energy purchase costs, system operation costs and curtailment penalties is specifically as follows:

[0014] The objective function of this model is

[0015]

[0016]

[0017] Where C is the total cost, Δt is the scheduling time interval, is the external energy purchase cost, For operating costs, Penalty cost for abandoned light; They are the output of cogeneration unit, diesel generator, gas boiler and distributed photovoltaic at time t. is the battery charging power at time t, is the battery discharge power at time t, c elec , c gasare the unit prices of purchased electricity and gas, respectively, CHP , δ CGU , δ GB , δ PV , δ BES The operating unit prices of cogeneration units, diesel generators, gas boilers, photovoltaics, and batteries are respectively: For purchased electricity, The amount of gas purchased is is the maximum output of distributed photovoltaic power at time t;

[0018] The optimal objective function satisfies the following flexibility constraints;

[0019]

[0020]

[0021]

[0022] Where λ e ,λ h Represent the fluctuation of electric load and heat load respectively; variables with superscript up represent upward flexibility supply / demand, variables with superscript dn represent downward flexibility supply / demand, SOC t-1 is the battery state of charge at time t-1, For its maximum value, SOC To its minimum value, Z BU is the battery capacity, η dc is the discharge efficiency, η ch For charging efficiency, is the maximum discharge power of the battery, is the maximum charging power, is the maximum power of the cogeneration unit, P CHP is the minimum power of the cogeneration unit, r CHP is the ramp rate of the cogeneration unit, is the maximum power of the diesel generator, P CGU is the minimum power of the diesel generator, r CGU is the diesel generator ramp rate, is the maximum power of the gas boiler, H GB is the minimum power of the gas boiler, r GB is the gas boiler ramp rate, is the CHP hotspot ratio, P load,t is the electrical load at time t, H load,t is the heat load at time t.

[0023] The beneficial effects of the present invention are:

[0024] The present invention proposes a modeling method for multi-energy equipment and electric heating networks, simplifying the physical model of thermoelectric coupling equipment with complex internal structure into a mathematical model that considers the energy conversion relationship between heat and electricity; proposes a method for constructing a source-load two-sided uncertainty model, using probability density functions to describe the randomness and volatility of electric heating loads and distributed photovoltaics, and constructing probability intervals for photovoltaic output and electric heating loads; proposes an optimal economic operation scheduling scheme that considers the time correlation of flexibility constraints and the thermal inertia of the heating network and uses the interior point method to solve it.

[0025] This invention breaks through the conventional idea of ​​traditional power systems that only considers the uncertainty of electric energy. It adds the uncertainty of thermal load and corresponding multi-energy equipment to the model, takes into account the time correlation of multi-energy flexibility and the thermal inertia of the heat network, improves the system precision and the accuracy of model simulation results, provides conditions for dealing with the volatility of electric and thermal loads and the uncertainty of new energy, and provides a reference for further research on the role of thermoelectric coupling characteristics in the flexibility of optimal scheduling of regional electric and thermal systems. It is in line with the current development trend of the power grid from single electric energy to multi-energy coupling. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 Provides a framework for the regional electric heating system;

[0027] Figure 2 Flowchart of the calculation method for optimizing the scheduling of regional electric heating systems considering the flexibility of electric heating equipment and the characteristics of the heating network. DETAILED DESCRIPTION

[0028] like Figure 1 This is the regional electric heating system framework of this embodiment.

[0029] like Figure 2 The figure shows a flow chart of a method for optimizing the scheduling of a regional electric heating system taking into account the flexibility constraints of electric heating equipment and the characteristics of the heating network according to the present invention.

[0030] (1) Modeling methods for multi-energy devices and electric heating networks

[0031] ① Combined heat and power unit

[0032] A cogeneration unit can simultaneously supply both electricity and heat, serving as the coupling point on the source side of a combined heat and power system. It utilizes the exhausted steam from the turbine after power generation, heating circulating hot water through a heat exchanger and supplying it to heat users for heating and other purposes. The high overall steam utilization rate allows the total efficiency, including both power generation and heating efficiency, to reach over 85%, effectively reducing environmental pollution. Cogeneration units generally include backpressure units and extraction-condensing units. Different types of units have different operating characteristics. When using a backpressure unit, the relationship between the electric and thermal power that a cogeneration unit can provide can be expressed as:

[0033] P CHP_Heat =λ CHP P CHP_ele

[0034] Among them, P CHP_ele is the electrical power of the cogeneration unit, P CHP_Heat is the thermal power of the cogeneration unit; CHP It is the heat-to-electricity ratio of the cogeneration unit, which is considered as a fixed positive value and ranges from 0 to 1.

[0035] ②Diesel generator set

[0036] A diesel generator set is a type of power generation equipment that uses diesel as fuel and a diesel engine as a prime mover. It has the advantages of short start-up time, easy operation and maintenance, low investment, and a wide range of applications. It can be used as a regular unit to provide production and living electricity to areas far from the power grid or industrial and mining enterprises, or as a backup unit to ensure a reliable and continuous power supply to hospitals, airports, and important industrial production enterprises. The output power of a diesel generator set meets the following constraints:

[0037]

[0038]

[0039]

[0040] Wherein, subscript i is the number of the small diesel generator set; They are the output active power and reactive power of small diesel generator sets respectively; The upper and lower limits of active power output of small diesel generator sets; The upper and lower limits of reactive power output of small diesel generator sets; It is the uphill and downhill climbing capability of small diesel engine sets.

[0041] ③Gas boiler

[0042] A boiler is a heat exchange device that uses heat generated by other energy sources to heat water (or steam) to a set temperature. Gas-fired boilers, primarily fueled by gas (mostly natural gas), convert chemical energy into internal energy, providing hot water or steam for production and daily life. Central heating stations may not generate enough heat to meet user needs. Therefore, other heat generation devices, primarily gas-fired boilers, are typically used. Their output model meets the following constraints:

[0043]

[0044]

[0045]

[0046] is the natural gas power consumed by the gas boiler at time t; is the heat output of the gas boiler at time t; η GB Heating efficiency of gas boiler; H GB It is the upper and lower limits of the thermal output of the gas boiler; It is the up-climbing and down-climbing capability of the gas boiler.

[0047] ④Battery

[0048] As the penetration rate of distributed renewable energy increases, in order to smooth out the randomness and volatility of renewable energy output and improve voltage quality, it is usually necessary to configure certain energy storage devices. Its state of charge represents the remaining amount of energy stored, and its dynamic process is shown in the following formula:

[0049]

[0050] Where: They represent the discharging and charging power of the energy storage system at time t respectively; Respectively represent the maximum discharge and charging power of energy storage; are the charge states of the energy storage system at time t+1 and time t, respectively; η ch ,η dc They represent the self-loss rate, charging and discharging efficiency of the energy storage device respectively; is the rated capacity of the energy storage.

[0051] ⑤ Distribution network linear power flow model

[0052] A linearized power flow model of radial distribution network is adopted, and the branch losses of distribution network are ignored in the simplification process.

[0053]

[0054] Where: k:j→k represents the set of nodes k to which power flows from node j; P ij and Q ij They represent the active and reactive power flowing from node i to node j respectively; P j and Q j are the active and reactive powers flowing to the load at node j respectively; R ij and X ij are the resistance and reactance of branch ij respectively; V i 、V j are the voltage amplitudes at node i and node j respectively.

[0055] ⑥ Thermal inertia model

[0056] It takes a certain amount of time for hot water to flow from the first section of the pipeline to the end. There is also a certain delay in the flow of heat energy from the first heat source station to the heat exchange station for heat energy distribution. Since the total length of the thermal pipeline can reach tens of kilometers, it sometimes takes more than ten minutes or even hours for heat energy to be generated from the heat source to be transferred to the user. The time delay of the heating network needs to be considered when formulating the scheduling plan.

[0057]

[0058] Where: n is a positive integer; M p is the total mass of hot water in pipe p; ρ is the density of water; m p is the mass flow rate of hot water in pipe p; D p and L p are the diameter and length of the pipeline p respectively; Δt is the scheduling time interval.

[0059] (2) Construction method of source-load two-sided uncertainty model

[0060] In order to construct flexibility demand constraints, based on the establishment of a multi-energy equipment power model, the volatility and randomness of source and load are considered, and a probability distribution model of distributed photovoltaic, thermal load and electric load is constructed.

[0061] ① Distributed photovoltaic probability distribution model

[0062] Photovoltaic power generation is a technology that uses the photovoltaic effect of solar cell semiconductor materials to directly convert solar radiation energy into electrical energy. Therefore, the output power of distributed photovoltaics will vary with the light intensity at the interface. Light intensity is a variable with a clear diurnal and highly random nature. Based on a review of historical photovoltaic output data and literature, it can be assumed that the probability distribution of light intensity satisfies the Beta distribution. Since the output power of distributed photovoltaics is approximately proportional to light intensity, the output power of distributed photovoltaics is also analyzed using this method, namely:

[0063]

[0064] Where, P is the distributed photovoltaic output power, P max is its maximum value. α and β are the two shape parameters of the Beta distribution, and the calculation formula is:

[0065]

[0066]

[0067] Where μ is the expectation of Γ distribution, σ 2The values ​​of α and β can be obtained from historical typical daily data, and the output uncertainty of distributed photovoltaics can be described based on this probability density function.

[0068] ②Electric load probability distribution model

[0069] The power of electric load often fluctuates randomly on a time scale. Based on the investigation of historical load data and literature, it can be considered that the probability distribution of electric load satisfies the normal distribution, that is:

[0070]

[0071] Where μ P and σ P Used to express the variance and expected value of active power, μ Q and σ Q Used to express the variance and expected value of active power.

[0072] ③Heat load probability distribution model

[0073] There is a certain correlation between thermal load and electrical load. The normal distribution can also be used to describe the probability distribution of thermal load on the time scale. However, due to the long time scale of thermal load change, the parameters are different.

[0074] (3) Optimal economic operation scheduling scheme considering flexibility constraints

[0075] Uncertainty on both the source and load sides affects the operation of the system and places demands on the system for flexibility. In order to fully utilize the energy supply regulation capability of the multi-energy equipment in the system, an optimal scheduling scheme for the regional electric heating system is proposed that takes into account the flexibility constraints of the electric heating equipment and the characteristics of the heating network. The objective function of this optimal operation scheme is:

[0076]

[0077]

[0078] Where C is the total cost, Δt is the scheduling time interval, is the external energy purchase cost, For operating costs, Penalty cost for abandoned light; They are the power output of cogeneration unit, diesel generator, gas boiler and distributed photovoltaic at time t. is the battery charging power at time t, is the battery discharge power at time t, c elec , c gas are the unit prices of purchased electricity and gas, respectively, CHP , δ CGU , δ GB, δ PV , δ BES The operating unit prices of cogeneration units, diesel generators, gas boilers, photovoltaics, and batteries are respectively: For purchased electricity, The amount of gas purchased is is the maximum output of distributed photovoltaic power at time t;

[0079] The optimal objective function satisfies the following flexibility constraints;

[0080]

[0081]

[0082]

[0083] Where λ e ,λ h Represent the fluctuation of electric load and heat load respectively; variables with superscript up represent upward flexibility supply / demand, variables with superscript dn represent downward flexibility supply / demand, SOC t-1 is the battery state of charge at time t-1, For its maximum value, SOC To its minimum value, Z BU is the battery capacity, η dc is the discharge efficiency, η ch For charging efficiency, is the maximum discharge power of the battery, is the maximum charging power, is the maximum power of the cogeneration unit, P CHP is the minimum power of the cogeneration unit, r CHP is the ramp rate of the cogeneration unit, is the maximum power of the diesel generator, P CGU is the minimum power of the diesel generator, r CGU is the diesel generator ramp rate, is the maximum power of the gas boiler, H GB is the minimum power of the gas boiler, r GB is the gas boiler ramp rate, is the CHP hotspot ratio, P load,t is the electrical load at time t, H load,t is the heat load at time t.

[0084] Introducing a time-dependent flexibility constraint, requiring flexibility supply to always exceed flexibility demand, allows the regional electric heating system to better cope with uncertain fluctuations in both the source and load sides. This optimization problem is a mixed-integer nonlinear optimization problem, and the interior point method can be used to solve the optimization model.

[0085] The above description of the specific implementation methods of the present invention in conjunction with the accompanying drawings is not intended to limit the scope of protection of the present invention. All equivalent models or equivalent algorithm processes made using the contents of the present invention specification and accompanying drawings, which are directly or indirectly applied to other related technical fields, are within the scope of patent protection of the present invention.

Claims

1. A method for optimizing the scheduling of a regional electric heating system considering the flexibility constraints of electric heating equipment and the characteristics of the heating network, characterized in that: The steps are as follows: First, based on the working principles and characteristics of multi-energy devices, a multi-energy device power model is constructed; considering the thermal inertia of the heat network, an electric heat network model is established; considering the equipment regulation characteristics, a flexibility supply constraint is established; then, based on historical data, a probability interval of distributed photovoltaic and electric heat loads is constructed, and a source-load two-sided uncertainty model is established to obtain a flexibility demand constraint; finally, considering the time correlation of the flexibility constraint and the thermal inertia of the heat network, an economic operation model is constructed with the goal of minimizing the sum of external energy purchase costs, system operating costs, and curtailment penalties. The model includes flexibility supply constraints and flexibility demand constraints. Solving this model can obtain the optimal scheduling plan for the regional electric heat system. The economic operation model, which aims to minimize the sum of external energy purchase costs, system operation costs, and curtailment penalties, is specifically: The objective function of this model is Where C is the total cost, Δt is the scheduling time interval, is the external energy purchase cost, For operating costs, Penalty cost for abandoned light; They are the output of cogeneration unit, diesel generator, gas boiler and distributed photovoltaic at time t. is the battery charging power at time t, is the battery discharge power at time t, c elec , c gas are the unit prices of purchased electricity and gas, respectively, CHP , δ CGU , δ GB , δ PV , δ BES The operating unit prices of cogeneration units, diesel generators, gas boilers, photovoltaics, and batteries are respectively t EXT is the purchased electricity, F t EXT The amount of gas purchased is is the maximum output of distributed photovoltaic power at time t; The optimal objective function satisfies the following flexibility constraints; Where λ e ,λ h Represent the fluctuation of electric load and heat load respectively; variables with superscript up represent upward flexibility supply / demand, variables with superscript dn represent downward flexibility supply / demand, SOC t-1 is the battery state of charge at time t-1, is its maximum value, SOC is its minimum value, Z BU is the battery capacity, η dc is the discharge efficiency, η ch For charging efficiency, is the maximum discharge power of the battery, is the maximum charging power, is the maximum power of the cogeneration unit, P CHP is the minimum power of the cogeneration unit, r CHP is the ramp rate of the cogeneration unit, is the maximum power of the diesel generator, P CGU is the minimum power of the diesel generator, r CGU is the diesel generator ramp rate, is the maximum power of the gas boiler, H GB is the minimum power of the gas boiler, r GB is the gas boiler ramp rate, is the CHP hotspot ratio, P load,t is the electrical load at time t, H load,t is the heat load at time t.

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