CN114330827A - Distributed robust self-scheduling optimization method for multi-energy flow virtual power plant and application thereof - Google Patents
Distributed robust self-scheduling optimization method for multi-energy flow virtual power plant and application thereof Download PDFInfo
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Abstract
The distributed robust self-scheduling optimization method of the multi-energy flow virtual power plant and the application thereof are based on Wasserstein distance and comprises the following steps: establishing a self-scheduling model of the multi-energy flow virtual power plant, and determining a target function of the self-scheduling model; establishing a multi-energy flow VPP operation model, an electric network model, a heat supply network model; establishing an air network model; establishing an HOME model; and (5) solving the self-scheduling model. After the processing by the method, the proposed distributed robust self-scheduling model can be directly solved through a commercial solver such as CPLEX to obtain an optimal scheduling scheme, so that the coordinated optimization regulation and control of the virtual power plant multi-energy flow considering carbon capture are realized, the CHP unit operation efficiency is improved, the thermoelectric decoupling is realized, the renewable energy consumption is improved, and the operation economy and flexibility of the whole system are improved.
Description
Technical Field
The invention relates to an optimization method and application thereof, in particular to a distributed robust self-scheduling optimization method of a multi-energy flow virtual power plant and application thereof.
Background
Today, energy system architectures rely heavily on various energy conversion technologies. Coupling elements such as a Combined Heat and Power (CHP) unit and a Heat Pump (HP) are integrated into a coupled electrical and thermal system, which are interdependent and have a significant impact on each other. In this regard, coordinated scheduling of coupled systems may improve energy utilization efficiency and reduce carbon dioxide emissions. However, the inrush of Distributed Energy Resources (DERs) greatly affects the safe and stable operation of power systems, such as energy balancing and blocking management. Furthermore, the traditional mode of operation of the CHP plant results in a substantial reduction of renewable energy. These difficulties present a significant challenge to the integration of multi-energy flow coupled systems.
One leading solution to the above problem is to use a Virtual Power Plant (VPP) platform that aggregates der clusters (e.g., s.yu, f.fang, y.liu, and j.liu, "unsimplients of virtual power plants: publications and counters," Applied Energy, vol.239, pp.454-470, 2019) based on advanced network communication, real-time detection and measurement techniques. VPPs are used not only to provide a platform for heterogeneous DERs, but also to integrate schedulable load to improve system flexibility. Existing literature on VPPs focuses primarily on energy management and scheduling optimization aspects of VPPs. In order to solve the optimization problem with uncertainty, the most basic optimization methods in the design of the VPP operation strategy include Stochastic Programming (SP) and Robust Optimization (RO). The SP method is based on the known probability density[5]Applied, this is difficult to obtain in practice and is therefore not suitable for most industrial applications. Furthermore, scenes generated by the SP method often result in a heavy computational burden. Meanwhile, the method of representing RO with an unqualified boundary always encounters difficulty in selecting an appropriate robust set (x. Lu, z. liu, l.ma, l.wang, k.zhou, and n.feng, "a robust optimization approach for optimal load distribution of community Energy hub," Applied Energy, vol.259, p.114195,2020), and the result is generally conservative.
In recent years, Distributed Robust Optimization (DRO) has been widely used to solve the optimization problem involving uncertainty [ p. Zhao, c.gu, y.xiang, x.zhang, y.shen, and s.li, "Reactive power optimization in integrated electronics and gas Systems," IEEE Systems Journal, pp.1-11,2020 ]. In combination with the advantages of SP and RO, the DRO-based approach implements a scheduling strategy under the worst uncertainty probability distribution in the fuzzy set. In document 8[ x.han, e.g. kardakos, and g.hug, "a distributed robust bidding for a with Power plant," Electric Power Systems Research, vol.177, p.105986, 2019], a two-layer DRO model was established to derive the optimal bidding strategy for a converged Wind Turbine (WT), with wind Power output as an uncertainty parameter, characterized by a series of distributed fuzzy sets defined. Similarly, a Two-stage DRO model is proposed in document 9[ y.zhang, j.le, f.zheng, y.zhang, and k.liu, "Two-stage distributed robust" Renewable Energy, vol.135, pp.122-135, 2019] for studying coordinated optimal scheduling of a multi-Energy coupling system with wind power uncertainty to minimize the expected operating cost. The uncertainty set established in the two papers above describes only moment information and does not limit its particular form of probability distribution. In document 10[ study on cooperative optimization scheduling of virtual cogeneration plants in loose sources [ D ]. university of north china electricity (beijing), 2021], a distributed robust optimization scheduling strategy for a virtual cogeneration plant is proposed, but only its economic optimization and does not consider environment-related factors taking into account carbon capture technology.
Although the VPP optimized scheduling problem has been reported in the literature before, there are still many challenges to be solved properly. Firstly, the accurate modeling of the multi-energy flow VPP is difficult due to the operational characteristics of various heterogeneous DERs and the coupling and dynamic characteristics of the grid, heat supply network and air network. Secondly, in the energy field optimization scheduling, the HP generally refers to an air conditioner, and participates in the VPP scheduling as a flexible power load, and the large heat pump should be regarded as a heating unit, consuming electric energy to release heat energy to meet the heat supply demand. The invention aims to solve the problems and provides a self-scheduling scheme of multi-energy flow VPP.
Disclosure of Invention
The problems to be solved by the invention are as follows:
(1) in the scheduling problem of the multi-energy flow virtual power plant, the uncertainty problem caused by the fluctuation of the wind-solar output load power is solved. Due to the intermittency and the time variability of wind energy and solar energy, the optimal scheduling scheme of the system cannot be obtained only according to the renewable energy source prediction output in the prior art, and the prediction error of the wind power output and the photovoltaic output is generally 20-30%. Due to the influence of environmental factors such as regions and the like, the wind speed has high randomness, so that the wind power generation has uncertainty. Also, photovoltaic power generation depends on the intensity of light, and is affected by sudden changes in weather, especially cloud changes, with both seasonal and diurnal modes. When the grid-connected capacity of renewable energy sources is increased, the generated energy with periodicity and randomness can generate certain influence on the safe, stable and economic operation of a power grid. Therefore, the uncertainty of wind-solar force needs to be described in a reasonable way.
(2) The problem of renewable energy consumption. Under the traditional scheduling mode of 'fixing electricity with heat', the electricity output of the CHP unit is positively correlated with the heat output of the CHP unit. Therefore, under the condition of high heat load in winter, the heat output of the CHP unit has to be increased, and the corresponding electric output is increased accordingly, so that the power generation capacity of the CHP unit can meet most of the power load requirements in the off-peak period, and the renewable energy power generation cannot be fully utilized, and the phenomenon of wind and light abandonment is generated. The conversion and coordination between the electricity energy and the heat energy can effectively improve the economical efficiency of the system and the consumption capacity of the renewable energy. The method for improving the flexibility of the system by the VPP mainly comprises the steps of adding equipment such as a coupling element and the like, adding energy storage devices such as heat storage and the like, building thermal characteristics, considering user comfort and the like.
(3) The problem of the dynamic characteristics of the heat supply pipe network. Compared with the transmission of electric energy, the transmission speed of hot gas in a network is low, the inertia is large, and therefore a certain transmission delay problem is caused, but the characteristics of slow dynamic and high time delay of a heat supply network are adopted, so that the heat supply network passively plays a role in energy storage, assists in peak clipping and valley filling of a power system, and consumes renewable energy. The natural gas network is much simpler than a heat supply network, but the modeling is too simple, so that the obtained planning result is inconsistent with the reality; modeling is too complex, which can cause difficulty in optimizing the solution. Therefore, the multi-energy flow virtual power plant coupling system needs to fully consider the problem of the multi-phase flow dynamic characteristics of the electric heating gas, and the original isolated power system is aggregated with a heat supply system and a gas supply system to research the energy complementation and the cooperative operation of the original isolated power system.
(4) Carbon emissions issues at the dual carbon target. The Carbon Capture and Storage (CCS) technology can flexibly control the CO2 capture force and energy distribution relationship, reduce the CO2 emission of the system, consume electric energy, increase the output of a thermal power plant, realize thermoelectric decoupling, and improve the peak regulation capability and the new energy consumption capability of the system. It is therefore contemplated to incorporate a carbon capture plant simultaneously to operate in conjunction with a multi-stream virtual power plant.
In order to solve the above problems, the invention discloses a distributed robust self-scheduling optimization method for a multi-energy flow virtual power plant, which is based on Wassertein distance (a known technology can be referred to, such as application number: CN2019112350507, publication number: CN 110797919A; application number: CN2019111485032, publication number: CN 110929399A, and the like), and is characterized in that: the method comprises the following steps:
step 1: establishing a self-scheduling model of the multi-energy flow virtual power plant, and determining a target function of the self-scheduling model;
step 2: establishing a VPP operation model;
and step 3: establishing a power grid model;
and 4, step 4: establishing a heat supply network model;
and 5: establishing an air network model;
step 6: establishing an HOME model;
and 7: and (5) solving the self-scheduling model.
The invention also discloses a distributed robust self-scheduling optimization method of the multi-energy flow virtual power plant, which is applied to the scheduling of the multi-energy flow virtual power plant.
Advantageous effects
(1) A distributed robust optimization method is adopted. The distributed robust optimization method combines the advantages of the SP and the RO, avoids the defect that the SP is difficult to obtain the real probability distribution of the uncertain factors, reduces the conservatism brought by the RO, and improves the flexibility of system scheduling.
(2) A large heat pump unit, a Thermal Energy Storage (TES) system, carbon capture and sequestration equipment and an HOME model are integrated into a traditional VPP, and a novel multi-energy-flow VPP self-scheduling scheme is designed by considering the coupling characteristics of a power grid, a heat grid and an air grid.
(3) The Wasserstein metric is applied to a data-driven fuzzy set of wind and solar power uncertainty and in a multi-power flow VPP system. Construction of the fuzzy set contains all possible realizations of uncertainty and controls the conservatism of the robust solution.
(4) In order to realize the aim of realizing double carbon in response to the call, trapping equipment is introduced into the multi-energy flow virtual power plant, and the carbon trapping equipment is used as schedulable load, so that the aims of energy conservation and emission reduction of the virtual power plant and cooperative optimization operation are fulfilled.
Detailed Description
A distributed robust self-scheduling optimization method for a multi-energy flow virtual power plant is based on Wasserstein distance and is characterized by comprising the following steps: the method comprises the following steps:
step 1: establishing a self-scheduling model of the multi-energy flow virtual power plant, and determining a target function of the self-scheduling model;
step 2: establishing a VPP operation model;
and step 3: establishing a power grid model;
and 4, step 4: establishing a heat supply network model;
and 5: establishing an air network model;
step 6: establishing an HOME model;
and 7: and (5) solving the self-scheduling model.
In the following we make a detailed statement about each of the above steps 1-7.
Step 1: establishing a self-scheduling model of the multi-energy flow virtual power plant, and determining a target function of the self-scheduling model;
distributed energy resources (i.e., wind, photovoltaic, CHP devices) are interconnected and employ demand-side management, such as Demand Response (DR) and electrical energy storage (BES) equipment, to provide electrical loads. HP and heat storage are integrated together to provide heat load with the CHP plant. In economic dispatch coupling multi-energy flow VPP systems, operators strive to maximize profits in the market today and minimize costs in the real-time market. Therefore, the step 1 further comprises the following steps: the self-scheduling model objective function comprises two stages, wherein in the first stage, the supply strategy and the unit combination of the multi-energy flow VPP are determined by utilizing the output predicted value of the renewable energy unit; in the second stage, the justification cost of the multi-power flow VPP is minimized under the worst renewable energy output conditions.
Notably, the multi-stream VPP should adhere to contracts that are signed one day in advance. Since the predicted value of renewable energy output inevitably produces a deviation, the multi-energy flow VPP system must adjust the decision of the controllable unit inside the multi-energy flow VPP. Therefore, the multi-stream VPP should first perform self-scheduling to reduce the transaction with the main network, which is considered a penalty. The min-max form is used to represent the regulatory cost for the worst implementation of renewable energy in an indeterminate set.
Unlike the electricity market, the contract entered into by the heating market is traded only once. Thus, the heat revenue for the multi-power flow VPP comes only from the CHP and HP in the day-ahead market. Note that CHP executes instructions according to a day-ahead decision because of its long response time. While HP acts as a "fast machine" and can be adjusted in real time. The power consumed by the HP can be seen as a flexible load. However, since HP is the only heat source that can be adjusted in real time, the capacity of HP is limited while satisfying the thermal comfort of the user. In summary, the proposed operational scheme objective function (1) is expressed as:
wherein
Respectively expressed as market transaction amount, CHP unit electric output, CHP unit heat output and CO absorption2Measuring, CHP unit start-stop variable, HP unit start-stop variable in the market at the day before,
respectively representing real-time market traffic volume, HP electric consumption, heat generated by HP, real-time electric demand, real-time market HP unit start-stop variables, BES charging, BES discharging, TES heat storage, TES heat release and indoor temperature;
in the set of uncertainty parameters
Respectively representing real-time wind power generation and photovoltaic power generation; the first phase of the objective function (1) consists of five parts,
and
the day-ahead revenues obtained from the electricity and heat markets respectively,
respectively the day-ahead market electricity price and the heat price,
sold electrical power and thermal power, respectively; IICHP，tRefers to the operating costs and startup/shutdown costs of the CHP plant,
is the pre-dispatching cost of HP unit, IIDB，tThe method refers to the environmental protection cost of multi-energy flow VPP, and H is calculated according to the pollution amount generated in the operation process of a CHP unitCCS，tRefers to the total operating cost of the CCS; in the second stage of the process,
in order to achieve the real-time market electricity price,
adjusting the electrical power for the real-time market with the goal of minimizing regulatory costs in the real-time market in the worst case; the cost includes a penalty cost
HP Start/shut Down cost
∏DB，tFor environmental protection cost, HCCS，tCost for carbon capture equipment sequestration, DR cost HDR，tAnd energy storage cost IIESS，TThe costs are expressed as follows:
wherein, χ0，χ1，χ2，χ3，χ4Respectively, the CHP cost factor is the CHP cost factor,
and
respectively indicate the starting and stopping cost of the CHP,
and
means start-stop cost of HP, dzIs the amount of pollutant emission, r, of item zzIs the z-th pollutant penalty cost,
is the amount of C02 being processed by the CCS device, rbFor sealing cost factor, λRTP，tTo participate in RTP penalty charges, AS，BS，CS，DS，ESFor the cost factors of BES and TES,
the electrical load when not participating in the RTP plan.
Step 2: establishing a VPP operation model;
1) a renewable power generator set: wind power generation systems and photovoltaic power plants are considered to be the primary renewable energy source of multi-stream VPP power generation. The power output of wind power generation and photovoltaic power generation is affected by wind speed and solar radiation, respectively. The intermittent and time-varying nature of renewable energy sources presents a significant difficulty to the scheduling process. The first stage uses predicted wind and solar power generation. Due to the differences between the predicted and realized renewable energy generation, an uncertainty set of renewable energy generation should be established.
2) CHP unit: the CHP plant generates and supplies heat simultaneously, and in the present invention, the CHP plant under consideration operates in a thermal load mode in which the amount of power generation is determined according to the thermal demand. The operating region of the CHP plant is limited by:
meanwhile, the upward/downward climbing rate of the CHP unit is limited as follows:
in the formula:
is respectively the maximum and minimum output, eta, of the CHP unitmax，ηminRespectively the maximum and minimum efficiency of the electricity-to-heat conversion of the CHP unit,
and
respectively the up/down capacity of the CHP unit.
3) An energy storage system: in a multi-energy flow VPP, an Energy Storage System (ESS) contains BES and TES for storing/excess power/heat or releasing stored energy. x is used to denote electricity or heat storage because they have similar characteristics. The state of charge (SoC) of a heat/electricity storage device is a key parameter describing its operating state and can be expressed as:
thus, the net power output of x over time interval [ t, t +1) is:
the limits of SoC capacity and charge/discharge power are:
in-type SoCX，t+1Characterizing the energy storage device state, Δ T represents a scheduling period,
respectively an upper limit and a lower limit of the x capacity of the energy storage device,
respectively show the charge-discharge efficiency of the battery,
and
and (4) upper and lower limits of charge and discharge power.
4) HP: the clean heating device converts electric energy into heat energy and is widely used in rural areas. The key indicator of HP is coefficient of performance (COP), which can be calculated as:
5) CCS: can capture CO2Reduction of CO2Emission, energy consumption of its operation
Comprises the following steps:
in the formula
Fixing energy consumption for CCS equipment
The operating energy required to process the unit of CO2,
respectively, for capturing CO2The upper and lower limits of (2).
6) RTP plan (real-time electricity rate plan): the program provides end users with power rate options by shifting peak loads to off-peak loads and replacing DERs in the Cogeneration VPPs. RTP planning is also a form of Demand Response (DR) to enhance the flexibility of multi-energy streaming VPP. Schedulable loads are integrated and provided to multi-energy flow VPP operators to exchange reduced energy bills. After applying RTP, the demand can be calculated as:
wherein phiDR，iThe cardinality of the RTP plan is characterized,
respectively representing the amount of RTP demand increase and decrease. The upper and lower limits of demand are limited by the following equation:
wherein,
a binary variable representing an increase and a decrease in demand respectively,
indicating the initial demand. It is worth mentioning that the daily increased demand must equal the decreased demand, and therefore
To prevent the RTP requirements from increasing and decreasing at the same time, it must be enforced:
thus, DR output
Can be calculated as:
note that the RTP plan is very flexible in combination with market prices. In the present invention, the RTP price is assumed to be the day-ahead market price, as it is constantly changing.
And step 3: establishing a power grid model;
1) day-ahead operation constraints of the grid: in the first phase of equation (1), the electrical and thermal output is optimized and the CHP output is determined to account for the uncertainty. In the present invention, direct power flow is employed and a power balance between the total generation and the electrical load must always be maintained on each bus of the grid, therefore:
in the formula, BmnFor susceptance, delta, between transmission lines mnm，δnIs the phase angle of the bus m, n.
2) Real-time operation constraint of the power grid: the second stage problem is used to compensate for renewable energy source prediction errors in real-time operation. The real-time rebalancing of power is as follows:
in the formula:
and
and respectively representing wind power generation and photovoltaic power generation predicted values.
And 4, step 4: establishing a heat supply network model
The heat network consists of a heat source, a heat transport network, end users on a primary heat network, and a secondary heat network. The two networks are connected by a heat exchanger, both of which comprise a water supply pipe and a water return pipe. Hot water is used as a medium to transfer the heat energy generated by the heat source from the primary heat network to the secondary heat network through each heat exchanger. The invention adopts the quality adjusting mode of the heat supply network, which not only can ensure the stability of the hydraulic working condition, but also is convenient for the operation in the actual operation.
1) Day-ahead operational constraints of the heat supply network:
the CHP units and HPs are used as a link between a power grid and a heat supply network and are heat sources of the multi-energy-flow VPP. The heat generated by the heat source is related to the temperature of the supply and return lines as follows:
wherein S ishsIs a collection of tubes associated with a heat source,
representing the mass flow of the water supply line, zeta is the switching parameter,
respectively representing the outlet temperature of the water supply pipeline and the outlet temperature of the water return pipeline, C is the specific heat capacity of water, and for each node in the heat supply network, according to kirchhoff's law, the mass flow of hot water entering the node is equal to the mass flow leaving the node, and the expression is as follows:
wherein,
which represents the mass flow of the return water pipe,
respectively, a starting pipe set and an end pipe set for node j. Considering hot water transport delay and heat energy loss, there are:
where τ is the time delay, κ is the loss factor,
and
mass flow rates of the inlet and the outlet of the water supply pipeline are respectively. According to the first law of thermodynamics, the thermal energy flowing into a node should be equal to the thermal energy flowing out of the node. Thus, the nodal temperature fusion may be described as
In the formula:
and
respectively representing the temperature of the joint j of the water supply and return pipeline.
2) Real-time operation constraint of the heat supply network: at this stage, the heat output of the CHP is fixed and the HP adjusts its output according to the needs of the system. When the actual renewable energy yield is higher than the predicted yield, the HP starts running and generates heat by consuming electricity. This indicates that:
in the formula,
respectively the outlet temperature of the real-time water supply and return pipeline.
The present invention omits real-time constraints of the heat grid because they are similar to corresponding day-ahead operational constraints.
And 5: establishing a gas network model
The partial model is used for describing the relation satisfied by the node pressure and the pipeline flow in the natural gas network. Steady flow f of natural gas pipeline rrCan be expressed as
Wherein, KrIs the pipeline constant; phi is the sign of the function;
defined as the pressure drop of the pipe r; sijFor characterizing the natural gas flow direction, when pi＞pjTaking +1 when the current value is positive, or taking-1 when the current value is negative;
the flow continuity equation of the natural gas network is
Agf＝L (30)
Wherein A isgRemoving a node-branch incidence matrix of a natural gas network containing a compressor pipeline; f is the natural gas flow of each pipeline; and L is the flow rate of each node.
Note the book
The pressure drop vector of the natural gas pipeline
Can be expressed as
Step 6: establishing HOME model
The HOMIE model introduces a detailed thermal model that models the heat flow occurring within a building, while taking into account the building and outside temperatures and occupant activity. The heat load comes from space heating of the building, and the HOME model is adopted for modeling. The heat net continuously transfers heat from the heat source through the heat pipe to maintain the indoor temperature at a set point.
The heat balance of a building is:
wherein c isair，ρairAnd VairRespectively, the isobaric heat capacity, density and volume of air. T isroom，tIs the indoor temperature at time T, Ttarget，tIs the target temperature. The heat emitted by occupants and electrical equipment and the radiation gain from transparent areas (e.g. windows) is considered as the internal heat gain of the building, i.e. the gain
Qint，t＝ωint，tSref (33)
Wherein, ω isint，tFor a specific internal gain, SrefIs an area for a building. Furthermore, by heat radiation Qirr，tAnd heat convection Qconv，tTwo major outdoor factors affecting indoor temperature are expressed as:
Qirr，t＝ωirr，tαtrans，tβangle，tSwindows (34)
Qconv，t＝naircairρairVair(Tout，t-Troom，t) (35)
wherein, ω isirr，tAs horizontal irradiation ratio, nairTo exchange rate, alphatrans，tAs transmission factor, betaangle，tAngle correction for the vertical region. SwindowsIs the window area, Tout，tIs the outdoor temperature. Convection heat exchange refers to the exchange of air, and heat conduction refers to the heat exchange between the indoor space and the outside space through the outer wall of the building. The heat transfer can be calculated as:
wherein S isarea，tMu is the total building areaiFor the U value, considering human comfort, the indoor temperature can vary within a certain range around the set point, with:
Tl≤Troom，t≤Tu (37)
wherein, TuAnd TlRespectively representing the upper and lower limits of the indoor temperature.
And 7: solution of self-scheduling model
The DRO-based approach implements the scheduling strategy in finding the worst case under a fuzzy set that contains a series of distributions characterized by some known property in the unknown distributions generated by the data set. To construct a suitable fuzzy set, the present invention employs a Wasserstein metric that describes the distance of different probability distributions by using Wasserstein spheres centered on empirical distributions.
As long as the radius is set reasonably, it can be considered that the true distribution should be contained in the constructed space sphere. And then the lowest risk that the optimization problem can bear under the distribution with the worst effect in the whole sphere is sought, namely the optimization result is the lower performance bound under the real sample distribution. In this respect, we seek to find the worst case optimal decision in the Wasserstein ball.
Defining a Wasserstein metric in the space N (xi) of all probability distributions P supported on the polyhedrons xi, two distributions P1And P2The Wasserstein distance between is defined as:
for all distributions P1，P2∈N(Ξ)，P1And P2The Wasserstein distance between them can be regarded as the transport plan II distribution P1To P2The cost of (a). The goal of the Wasserstein distance is to seek an optimal transportation plan with the lowest cost. Arbitrary norm | xi1-ξ2And | represents the transportation cost. Thus, the fuzzy set may be defined as:
can be regarded as a Wasserstein sphere with radius rho, and is distributed according to experience
As the center. By reasonably setting the radius rho, the unknown real distribution can be contained in the fuzzy set
In (1). In addition, the radius also affects the conservatism of the decision. The larger Wasserstein sphere radius results in a solution that relies less on certain features of the known data set for decision making and is more robust against sampling errors. Therefore, the method is adopted to describe the uncertainty of the renewable energy output and construct the fuzzy set of the wind power output and the photovoltaic output.
For convenience of description, we describe in matrix form:
s.t.Ax≤d，Bx＝e (41a)
Gy+Hξ≤f (41b)
Jx+Ky+Lξ＝g (41c)
x and y are first stage and second stage decision variables, respectively, and ξ represents an uncertainty variable. The distribution of uncertain variables being contained in fuzzy sets
In (1). Constraints (41a) are from constraints (3) - (5) and (22) - (27) associated with the decision variables of the first stage. Constraints (41b) are from constraints (6) - (19) and (28) - (37) associated with decision variables of the second stage. The constraint (41c) is a constraint of (20) and (21).
Please note that we need to combine the original objective function(1) Converted to an approximate form that is easy to handle. Another way to deal with the absolute values in (1) is to introduce two new non-negative variables
And
let
Then we use
Instead of the former
The expected problem of the inner worst case in the Wasserstein ambiguity set (40) can be reduced to a cone program. Hypothetical polyhedron
M is a matrix and M is a vector of appropriate dimensions. The inner layer problem corresponds to:
wherein λoAre dual variables of the constraint (39),
and
is an auxiliary variable, | · | | non-conducting phosphor*Representing a two-norm of | · |.
After the processing by the method, the proposed distributed robust self-scheduling model can be directly solved through a commercial solver such as CPLEX to obtain an optimal scheduling scheme, so that the coordinated optimization regulation and control of the virtual power plant multi-energy flow considering carbon capture are realized, the CHP unit operation efficiency is improved, the thermoelectric decoupling is realized, the renewable energy consumption is improved, and the operation economy and flexibility of the whole system are improved.
The invention also discloses a distributed robust self-scheduling optimization method of the multi-energy flow virtual power plant, which is applied to the scheduling of the multi-energy flow virtual power plant.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are merely illustrative of the principles of the invention, but that various changes and modifications may be made without departing from the spirit and scope of the invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. A distributed robust self-scheduling optimization method for a multi-energy flow virtual power plant is based on Wasserstein distance and is characterized by comprising the following steps: the method comprises the following steps:
step 1: establishing a self-scheduling model of the multi-energy flow virtual power plant, and determining a target function of the self-scheduling model;
step 2: establishing a VPP operation model;
and step 3: establishing a power grid model;
and 4, step 4: establishing a heat supply network model;
and 5: establishing an air network model;
step 6: establishing an HOME model;
and 7: and (5) solving the self-scheduling model.
2. The distributed robust self-scheduling optimization method for the multi-energy flow virtual power plant according to claim 1, characterized by comprising the following steps: the step 1 further comprises the following steps: the self-scheduling model objective function comprises two stages, wherein in the first stage, the supply strategy and the unit combination of the multi-energy flow VPP are determined by utilizing the output predicted value of the renewable energy unit; in the second stage, the justification cost of the multi-power flow VPP is minimized under the worst renewable energy output conditions.
3. The distributed robust self-scheduling optimization method for the multi-energy flow virtual power plant according to claim 2, characterized by comprising the following steps: the objective function (1) is expressed as:
wherein
Respectively expressed as market transaction amount, CHP unit electric output, CHP unit heat output and CO absorption2Measuring, CHP unit start-stop variable, HP unit start-stop variable in the market at the day before,
respectively representing real-time market transaction amount, HP consumed electric quantity, heat generated by HP, real-time electric demand, real-time market HP unit start-stop variables, BES charging, BES discharging, TES heat storage, TES heat release and indoor temperature;
in the set of uncertainty parameters
Respectively representing real-time wind power output and photovoltaic output; the first phase of the objective function (1) consists of five parts,
and
the day-ahead revenues obtained from the electricity and heat markets respectively,
respectively the day-ahead market electricity price and heat price,
sold electrical power and thermal power, respectively; II typeCHP,tRefers to the operating costs and startup/shutdown costs of the CHP plant,
pre-scheduling cost of HP unit, piDB,tThe environmental protection cost of the multi-energy VPP is calculated according to the pollution amount generated in the operation process of the CHP unitCCS,tRefers to the total operating cost of the CCS; in the second stage of the process,
in order to achieve the real-time market electricity price,
adjusting the electrical power for the real-time market with the goal of minimizing regulatory costs in the real-time market in the worst case; the cost includes a penalty cost
HP startup/shutdown costs
ΠDB,tLow cost for environmental protectionCCS,tFor carbon capture equipment sequestration cost, DR cost piDR,tAnd energy storage cost piESS,tThe costs are expressed as follows:
wherein, χ0,χ1,χ2,χ3,χ4Respectively, the CHP cost factor is the CHP cost factor,
and
respectively indicate the starting and stopping cost of the CHP,
and
means start-stop cost of HP, dzIs the amount of pollutant emission, r, of item zzIs the z-th pollutant penalty cost,
is the amount of CO2 being processed by the CCS plant, rbFor sealing cost factor, λRTP,tPenalizing fees for participation in RTPWith, AS,BS,CS,DS,ESThe cost factors of BES and TES.
The electrical load when not participating in the RTP plan.
4. The distributed robust self-scheduling optimization method for the multi-energy flow virtual power plant according to claim 1, characterized by comprising the following steps: the step 2 further comprises the following steps: the multi-energy flow VPP model comprises a renewable energy generator set, a CHP set, an energy storage system, HP, CCS and RTP plans.
5. The distributed robust self-scheduling optimization method for the multi-energy flow virtual power plant according to claim 4, characterized by comprising the following steps: a renewable power generator set: establishing an uncertainty set of renewable energy power generation;
CHP unit: the CHP unit generates and supplies power simultaneously, and the CHP unit under consideration operates in a mode in which the amount of power generation is determined according to the heat demand. The operating region of the CHP plant is limited by:
meanwhile, the upward/downward climbing rate of the CHP unit is limited as follows:
in the formula:
is respectively the maximum and minimum output, eta, of the CHP unitmax,ηminMaximum and minimum efficiency of electricity-to-heat conversion of CHP unit
And
respectively the ascending/descending capacity of the CHP unit;
3) an energy storage system: in a multi-energy flow VPP, an Energy Storage System (ESS) includes BES and TES for storing/excess power/heat or releasing stored energy; x is used to denote electricity or heat storage because they have similar characteristics; the state of charge (SoC) of a heat/electricity storage device is a key parameter describing its operating state and can be expressed as:
thus, the net power output of X over time interval [ t, t +1) is:
the limits of SoC capacity and charge/discharge power are:
in-type SoCX,t+1Characterizing the energy storage device state, Δ T represents a scheduling period,
respectively an upper limit and a lower limit of the X capacity of the energy storage device,
respectively show the charge-discharge efficiency of the battery,
and
upper and lower limits of charge and discharge power;
4) the key index is coefficient of performance (COP), which can be calculated as:
5) CCS: can capture CO2Reduction of CO2Emission, energy consumption of its operation
Comprises the following steps:
in the formula
Fixing energy consumption for CCS equipment
Energy consumption for operating the plant for treating the CO2 unit，
Respectively, for capturing CO2The upper and lower limits of (d);
6) RTP plan: the demand is calculated as:
wherein phiDR,iThe cardinality of the RTP plan is characterized,
P D,trespectively representing the increase and decrease of the RTP requirement; the upper and lower limits of demand are limited by the following equation:
wherein,
ψ tbinary variables representing the increase and decrease in demand, respectively, since the increased demand must equal the decreased demand each day, therefore
To prevent the RTP requirements from increasing and decreasing at the same time, it must be enforced:
thus, DR output
Can be calculated as:
6. the distributed robust self-scheduling optimization method for the multi-energy flow virtual power plant according to claim 1, characterized by comprising the following steps: the step 3 further comprises the following steps: the grid model includes two parts, a day-ahead operation constraint and a real-time operation constraint.
Day-ahead operational constraints of the heat supply network:
the heat generated by the heat source is related to the temperature of the supply and return lines as follows:
wherein S ishsIs a collection of tubes associated with a heat source,
the mass flow in the water supply line, ζ is the switching parameter,
respectively representing the outlet temperature of the water supply pipeline and the outlet temperature of the water return pipeline, C is the specific heat capacity of water, and for each node in the heat supply network, according to kirchhoff's law, the mass flow of hot water entering the node is equal to the mass flow leaving the node, and the expression is as follows:
wherein,
which represents the mass flow of the return water pipe,
respectively a starting pipeline set and a tail end pipeline set of the node j; considering hot water transport delay and heat energy loss, there are:
where τ is the time delay, κ is the loss factor,
and
respectively the mass flow of an inlet and an outlet of a water supply pipeline, and according to a first law of thermodynamics, the heat energy of an inflow node is equal to the heat energy of an outflow node; thus, the nodal temperature fusion may be described as
In the formula:
and
respectively representing the temperature of the joint j of the water supply and return pipeline.
Real-time operation constraint of the heat supply network: at this stage, the heat output of the CHP is fixed, and the HP adjusts its output according to the needs of the system; when the actual renewable energy yield is higher than the predicted yield, HP starts running and generates heat through electricity consumption, which indicates:
in the formula,
respectively the outlet temperature of the real-time water supply and return pipeline.
7. The distributed robust self-scheduling optimization method for the multi-energy flow virtual power plant according to claim 1, characterized by comprising the following steps: the step 4 further comprises the following steps: the heat supply network model comprises day-ahead and real-time operation constraints; the day-ahead operation constraint of the heat supply network comprises the following contents:
the CHP plant and HPs act as a tie between the grid and the grid, and are the heat sources for the multi-energy flow VPP. The heat generated by the heat source is related to the temperature of the supply and return lines as follows:
wherein S ishsIs the set of pipes associated with the heat source, ζ is the conversion parameter,
respectively representing the outlet temperature of the water supply pipeline and the outlet temperature of the water return pipeline, C is the specific heat capacity of water, and for each node in the heat supply network, according to kirchhoff's law, the mass flow of hot water entering the node is equal to the mass flow leaving the node, and the expression is as follows:
wherein,
respectively represent the mass flow to the return pipe,
respectively a starting pipeline set and a tail end pipeline set of the node j; considering hot water transport delay and heat energy loss, there are:
wherein tau is time delay and kappa is loss coefficient, and according to the first law of thermodynamics, the heat energy flowing into the node is equal to the heat energy flowing out of the node; thus, the nodal temperature fusion may be described as
The real-time operation constraints of the heat supply network comprise the following contents: at this stage, the heat output of the CHP is fixed, and the HP adjusts its output according to the needs of the system; when the actual renewable energy yield is higher than the predicted yield, the HP starts to run and generates heat by consuming electricity; this indicates that:
8. the distributed robust self-scheduling optimization method for the multi-energy flow virtual power plant according to claim 1, characterized by comprising the following steps: the step 5 further comprises the following steps: and the gas network model is used for describing the relation met by the node pressure and the pipeline flow in the natural gas network.
9. The distributed robust self-scheduling optimization method for the multi-energy flow virtual power plant according to claim 1, characterized by comprising the following steps: the step 6 further comprises the following steps: the HOME IE model is established, which simulates the heat flow occurring in the building, and takes into account the building and the outside temperature as well as the activities of the occupants; the step 7 further comprises the following steps: firstly, the absolute values are arranged into a general form, then the absolute values are processed, the inner layer problem is simplified again, and finally a commercial solver is adopted for solving.
10. The distributed robust self-scheduling optimization method for the multi-energy flow virtual power plant according to any one of claims 1 to 9 is applied to scheduling of the multi-energy flow virtual power plant.
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