A planning method for low-energy building distribution system considering source, grid, load and storage characteristics

Abstract

The present invention discloses a method for planning a power distribution system for a low-energy building that takes into account the characteristics of source, grid, load and storage. According to the characteristics of each part of the "source-grid-load-storage" involved in the power distribution system, an energy consumption model during the operation of the building and a total cost model of the distribution part during the service life of the building are established. This forms a "photovoltaic + energy storage" planning problem that solves the optimal cost under limited energy consumption, which can be solved using the Benders decomposition method. Considering that there are multiple alternatives for the actual design parameters, this paper uses the sensitivity model of the original planning problem to achieve parameter optimization, and formulates an overall process of two-step iteration of planning and parameter adjustment. Finally, the case study of a single-story exhibition hall shows that the planning method in this paper is practical and effective, which can reduce costs while limiting the energy consumption of building operation and promote the promotion of low-energy buildings.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution systems, and in particular to a method for planning a low-energy building power distribution system taking into account source, grid, load and storage characteristics. Background Art

[0002] With the deepening of urbanization and the continuous improvement of residents' living standards, the energy consumption of building operation has climbed to 30% of the total social energy consumption. The design and promotion of ultra-low and near-zero energy buildings has become an urgent need of society.

[0003] At present, there are two main ways to reduce the energy consumption of building operation: "increasing energy" and "saving energy". The former refers to making full use of renewable energy, and the latter refers to saving energy and reducing load demand through passive building design, HVAC system design, and power distribution system design. In response to this, some studies have proposed to reduce energy consumption by establishing an integrated energy system, and pointed out design ideas and suggestions at different ports of "source-grid-load-storage". This article attempts to go a step further and explore the planning method of reducing energy consumption and costs at the same time by optimizing the modeling at each end of "source-grid-load-storage", so as to provide help for the subsequent design and promotion of such buildings. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a low-energy building distribution system planning method considering the source-grid-load-storage characteristics in view of the above-mentioned defects of the prior art, which specifically includes the following steps:

[0005] 1) Model the electrical characteristics and economic costs of the design details of each part of the source, grid, load and storage respectively; the modeling process includes establishing a building power system model, a building distribution network model, a building power load model and a building energy storage system model; among them, the establishment of the building power system model includes photovoltaic power generation and purchased electricity.

[0006] Photovoltaic power generation model establishment:

[0007] The power of building photovoltaic power generation depends on natural factors such as light, as well as human factors such as the configuration of power generation components. Factors such as light, temperature, and wind speed have greater randomness within the time scale from hours to a single day, but have stronger regularity throughout the year, covering conditions that are not conducive to power generation such as continuous rain, fog, and haze. Therefore, L input scenarios for photovoltaic power generation can be generated by using historical data from a single or multiple years, or by using random algorithms.

[0008] In addition, it is necessary to comprehensively consider the photovoltaic installation conditions at different locations of the building, the average annual power generation per unit area of ​​the components, and the cost per unit area, and select the component type for each area; assuming that the building contains M power generation areas, and the construction area of ​​each part is A j satisfy:

[0009]

[0010] In the formula, is the upper limit of the installation area and available area of ​​the jth block. If the whole year is divided into N periods, the upper limit of the photovoltaic system power generation in the kth period in the i-th input scenario can be calculated using meteorological data:

[0011]

[0012] In the formula is the power generation per unit area of ​​the photovoltaic module in the jth block area and the kth time period in the i-th input scenario; the maximum power generation capacity of the photovoltaic system decays year by year, and the function of the relative initial capacity ratio changing with time is ρ PV (t), and the PV modules reach their service life T PV If the power generation capacity is replaced after 20 years (usually 20 years), the upper limit of power generation capacity in the corresponding scenario and time period in year t is:

[0013]

[0014] where mod is the remainder; therefore, the photovoltaic system power generation P in the kth period under the i-th scenario in the t-th year is PV,t,i,k satisfy:

[0015]

[0016] Assume that the fixed investment cost per unit area, annual average operation and maintenance cost, and recovery cost of the jth region are c PVinv,j 、c PVopr,j and c PVrec,j ; then the fixed investment cost C PVinv , Operation and maintenance cost C PVopr and recovery cost C PVrec satisfy:

[0017]

[0018] Where r dis is the annual discount rate, and the useful life of the building is T B (The 40-year useful life of commercial land can be used), and the total investment in the photovoltaic power generation system within this period is:

[0019]

[0020] It should be noted that due to the minimum area limit of the components, the grid-connected converter and other control devices need to be selected from the existing specifications and cannot be specially customized. The actual photovoltaic system power generation and economic cost are not piecewise linear functions of the component installation area. In order to facilitate the solution of capacity planning problems, this paper adopts this type of approximation in the modeling process of here and other distribution systems. In the design process, it is necessary to appropriately enlarge the capacity based on the model for selection.

[0021] Model establishment of purchased electricity:

[0022] The electricity required for daily operation of low-energy buildings is mainly supplied by renewable energy, and the shortfall needs to be purchased from power supply companies. When planning the regional distribution network, the network capacity will be designed according to the land use type to ensure the supply of daily building needs. At present, in my country's urban power distribution, passive networks are still the main ones, and the power flow is unidirectional, and the building feedback power is not accepted. Therefore, for the k-th period of the purchased power P in the i-th scenario in the t-th year, Grid,t,i,k Only the lower limit is applied:

[0023] P Grid,t,i,k ≥0 (1-7);

[0024] Ignoring the impact of rising building electricity prices, only considering the short-term peak-valley differences in some regions, the electricity price for the kth period in the first year after the building is completed is c Grid,k , the cost of purchasing electricity inside and outside the building during its service life can be estimated as:

[0025]

[0026] 2) Optimize models and solution methods, select energy-saving technologies and equipment, and solve the problem of minimum energy consumption and economically optimal capacity planning;

[0027] 3) Calculate the sensitivity of economic objectives to the adjustment of design detail parameters;

[0028] 4) Solve the economic optimal design problem and determine the adjustment of design details.

[0029] The first step calculation model is a linear approximation in the neighborhood of point x, and there are errors. After the calculation is completed, it must be substituted back into the second step capacity planning problem for verification, and the third and fourth steps must be re-experienced to verify whether the design details need further adjustment.

[0030] Furthermore, the establishment of the building power distribution network model includes the following steps:

[0031] The design goal of the building power distribution network is to minimize direct costs such as investment and construction, equipment maintenance, and recycling while ensuring power supply security, while reducing the indirect cost growth caused by operating network losses to other parts of the system.

[0032] The distribution network is divided into three parts: near source, near load, and near storage according to the topological connection position; the capacity of the near source part is proportional to the corresponding power source, and is composed of two parts connected to photovoltaic and connected to the external network; the capacity of the near load part is determined by the load and can be calculated according to the required coefficient method; the capacity of the near storage part is proportional to the energy storage; after determining the selection scheme, the direct cost can be calculated based on the total capacity of the distribution transformer equipment. The direct cost of the near source part is:

[0033] C Net / Gen =c Net / PV E PVsum +C Net / Grid (1-9);

[0034] Among them, c Net / PV is the cost of inverter and other equipment corresponding to unit photovoltaic capacity, C Net / Grid is the direct cost of the grid-side transformer / converter, E PVsum is the total installed capacity of photovoltaic power, and the installed capacity per unit area of ​​the jth block is e PV,j , then:

[0035]

[0036] In addition, the direct costs of the near-storage part are:

[0037]

[0038] Among them, c Net / BESS is the cost of converter and other equipment corresponding to unit energy storage capacity, E BESS is the energy storage capacity, Assume C is the upper capacity limit. Net / Load is the direct cost of the near-load part, then the total direct cost of the distribution network is:

[0039] C Net =C Net / Gen +C Net / Load +C Net / BESS (1-12);

[0040] The cost of operating network losses cannot be calculated directly, which is reflected in the increase in power generation and energy storage costs caused by network losses. Network losses are not only related to network parameters, but also affected by power flow distribution. However, due to the small number of metering points in the building distribution network and the lack of historical records of power flow data, it is difficult to accurately estimate network losses in the long-term operation process in the future. After the distribution network equipment selection plan is determined, the rated load state of each device can be selected to calculate the power conversion rate of the three parts of the distribution network near the source, near the load, and near the storage, respectively, and this can be used as a basis for evaluating the network loss level under different operating conditions of the distribution network; record that in the kth period of the i-th specific scenario in the tth year, the charging and discharging powers of the energy storage system are P respectively. BESS-c,t,i,k and P BESS-d,t,i,k , the load power is PLoad,t,i,k ; Then the power balance equation of the distribution network is:

[0041]

[0042] Among them, η PV2Net and η Grid2Net are the conversion rates of photovoltaic power and external power grid power to building distribution network, η Net2BESS and η BESS2Net are the power conversion rates from distribution network to energy storage and from energy storage to distribution network, respectively, Net2Load is the power conversion rate from distribution network to load, which are all less than 1. According to the principle of energy conservation and ignoring the electromagnetic waves radiated by the distribution network, the heating power of the distribution network can be obtained as:

[0043]

[0044] Furthermore, the establishment of the building power load model includes the following steps:

[0045] The establishment of the building power load model includes public load, user load and demand-side response, wherein the establishment of the public load and user load model includes the following steps:

[0046] Public loads include the HVAC, water supply and drainage systems in the building, as well as elevators, lighting, broadcasting, and monitoring systems, which are invested and maintained by the owner. Another part of the load in the building is connected through sockets, and the ownership and capacity of the electrical appliances cannot be predicted. When designing a building, public loads can be included in the integrated optimization, so the direct costs of selecting energy-saving equipment must be included in the total cost. In order to estimate the indirect costs caused by load energy consumption, it is necessary to model the annual power consumption of public loads and user loads. In this regard, this article discusses three parts:

[0047] 1. For the cooling and heating loads of building fresh air, air conditioning, etc., the direct cost is recorded as C Thm ; Affected by the outdoor environment, power changes show obvious seasonal characteristics. At present, there are mature simulation software in China that solves the building energy consumption by establishing models such as steel structure, external enclosure, and HVAC system. After inputting external wind, light, heat, and building internal heat parameters, the energy consumption simulation software can solve the power consumption of cooling / heating load in the kth period in the i-th specific scenario in the tth year as P Th-c,t,i,k / P Th-h,t,i,k ; At this time, the power load in the building and the heating power of the energy storage system are Q Load,t,i,k and Q BESS,t,i,k , then the total heating power of the power distribution system satisfies:

[0048]

[0049] 2. For loads such as building power and public lighting, there are R categories, of which the direct cost of the rth category is recorded as C Elc,r The power change of this type of load is related to work and vacation. It can be estimated by setting the usage frequency or based on the usage of similar loads in similar buildings and the construction scale. The power consumption of the rth load in the kth period in the i-th specific scenario in the t-th year is recorded as P Elc,t,i,k,r , the heating power is Q Elc,t,i,k,r ;

[0050] 3. For user-related loads, including socket loads and electric vehicle charging loads, the direct cost of such loads should not be considered. Their power situation can also be estimated by setting the usage frequency, or based on the usage of similar loads in similar buildings, combined with the construction scale, and the power consumption in the kth period in the i-th specific scenario in the t-th year is recorded as P Plg,t,i,k , and convert the indoor equipment heating power Q according to the proportion Plg,t,i,k When studying the total energy consumption of a building, this part of energy consumption should be excluded, and the unit price of the power supply fee charged by the owner to the user is c Plg , then the sum of the owner's electricity sales revenue in all scenarios is:

[0051]

[0052] The design of the building's power distribution system also includes supporting power designs such as fire emergency, and the capacity of each substation must reserve a margin for it; this article focuses on the energy consumption and costs generated by the daily construction and use of the building, and does not expand on the energy consumption generated by this part.

[0053] The model establishment of the demand-side response includes the following steps: the requirements of building users for environmental conditions are flexible, and the power of various power loads in public buildings can be adjusted within a certain range to achieve overall energy saving. Among them, the air conditioning load accounts for a large proportion of the total, and it is convenient for centralized management, which is the key to achieving the demand-side response of building electricity. Suppose the HVAC system energy consumption simulation software solves the average building temperature in the kth period in the i-th specific scenario in the tth year as Θ t,i,k , the upper and lower limits of comfort are required to be and θ t,i,k If the additional control amount brought by the cold and heat storage equipment is not considered, then once the demand side response is determined, the power consumption change ΔP Thm,t,i,k,r , the average temperature change ΔΘ can be solved by the approximate linear deterministic model t,i,k Specific requirements:

[0054]

[0055] Among them, α t,i,k , β t,i,k and γ t,i,kis the sensitivity coefficient of the system temperature at a specific moment, which is related to the environment, enclosure and HVAC system parameters and operation mode. Heat in the building configured for HVAC system energy consumption simulation, ΔP Th-c,t,i,k / ΔP Th-h,t,i,k is the cooling / heating electricity load increment. Since it is difficult to predict the building temperature and cooling / heating load at the hourly sampling interval on a long time scale in actual operation, the cooling and heating demand side management often uses one day as the optimization cycle. For the convenience of calculation, the impact of the temperature change at the end of the previous day on the next day is ignored, and the corresponding time coefficient α t,i,k The value is 0. The actual building temperature is not uniform and contains other indicators besides temperature, so this model can only be used as a reference.

[0056] The implementation of demand-side management inevitably requires the configuration of measurement sensors and automatic control devices in the building, as well as energy management servers. Therefore, the direct cost C brought by this part of the equipment must also be calculated. DR , power consumption P DR,t,i,k And heating power Q DR,t,i,k (The power in the kth period in the i-th specific scenario in the t-th year can be regarded as constant power).

[0057] So far, the total power consumed by the load is:

[0058]

[0059] In addition, the total heating power of the supplementary load is expressed as follows:

[0060]

[0061] In addition, the total direct cost of the building's electrical load is:

[0062]

[0063] Furthermore, the establishment of the building energy storage system model includes the following steps:

[0064] First, let’s focus on the energy consumption of the energy storage system. The energy consumption of the battery energy storage system in the building mainly consists of two parts: the battery pack and the heat dissipation equipment. Therefore, it is necessary to examine the characteristics of the two parts in the kth period under the i-th scenario in the t-th year. First, let the energy retained by the battery pack at this time be F Btr,i,j,k The charging and discharging powers of the battery pack are P Btr-c,t,i,k and P Btr-d,t,i,k (The capacity planning problem in the following text can automatically ensure that the two are not non-zero at the same time, so it can avoid adding dual constraints to the model.) Then the energy F retained in the k+1th period after the unit time interval △t can be obtained Btr,i,j,k+1 satisfy:

[0065]

[0066] where η Btr-s is the battery self-discharge rate per unit time interval, η Btr-c and η Btr-d are the charging and discharging efficiencies of the battery pack, both of which are less than 1. In addition, considering the attenuation of the battery pack capacity during use and avoiding excessive loss caused by deep discharge, the upper and lower limits of the ratio of retained energy to initial capacity are set. and λ Btr Since it is difficult to predict building electricity consumption at the hourly sampling interval on a long time scale in actual operation, energy storage charging and discharging is often optimized on a daily basis. The last two rows of constraints ensure that the battery energy at the beginning and end of each day is consistent with H Btr .

[0067] In addition, according to the principle of energy conservation, it is not difficult to deduce that the heating power of the battery pack in the kth period under the i-th scenario in the t-th year is:

[0068]

[0069] Secondly, let the inverse of the energy efficiency index of the heat dissipation equipment be κ Bcl , the power consumption is:

[0070] P Bcl,t,i,k =κ Bcl Q Btr,t,i,k (1-23);

[0071] It still releases heat to the overall environment inside and around the building, and the heating power is:

[0072] Q Bcl,t,i,k =P Bcl,t,i,k (1-24);

[0073] At this point, the overall power consumption equation of the energy storage system can be obtained:

[0074]

[0075] And the total heating power of the energy storage system:

[0076] Q BESS,t,i,k =Q Btr,t,i,k +Q Bcl,t,i,k (1-26);

[0077] Next, we discuss the economic model of the energy storage system. Assume that the fixed investment cost per unit capacity of energy storage, the annual average operation and maintenance cost, and the recovery cost are c BESSinv 、c BESSopr and c BESSrec If the service life of the energy storage system is T BESSYears (usually 5 years), the fixed investment cost C BESSinv , Operation and maintenance cost C BESSopr and recovery cost C BESSrec satisfy:

[0078]

[0079] The total investment in the energy storage system during the service life of the building is:

[0080]

[0081] Furthermore, the step 2) includes the following specific steps: In view of the fact that the direct costs of new energy power sources, converters, energy-saving loads and energy storage devices, as well as the economic factors such as the price of electricity sold from the external network are still uncertain in the future, in the design process of low-energy buildings in this paper, priority is given to reducing energy consumption during building operation, and secondly, attention is paid to the economic cost of the building distribution system. In the photovoltaic and energy storage capacity planning problems, parameters such as network structure, transformation gateways and load types, capacity size, etc. are temporarily fixed and left to be adjusted in the subsequent distribution system design steps. Let the photovoltaic installation area, energy storage capacity, daily initial energy storage power and electrical / heat / temperature operating status of the distribution system in the planning scope involved in equations (1-1)-(1-26) be recorded as x, then the main optimization objective of the capacity planning problem is:

[0082]

[0083] The secondary objectives are:

[0084]

[0085] Formulas (1-1)-(1-26) are uniformly marked as constraints:

[0086] g(x)≤0 (2-3);

[0087] Therefore, the optimization model can be written as:

[0088]

[0089] The model contains multiple layers of optimization that are difficult to solve directly, but can be decomposed into two independent mixed integer linear programming problems, which are solved sequentially:

[0090]

[0091] If the energy consumption requirements are clear and wide before design, the upper limit of the corresponding constraint in Step 2 can be modified as appropriate. The linear programming solution method is very mature, but the optimization time span in this article is long and the scale is large, so it cannot be solved directly. The Benders decomposition method can be used to divide the optimization variables into global effects (such as photovoltaic area, energy storage capacity, etc.) and local effects (such as electricity and thermal power within a single day, etc.), and decompose the problem into two main and subordinate parts. The first constraint in Step 2 represents that the total long-term energy consumption does not exceed the upper limit, and it also needs to be decomposed into the energy consumption upper limit of each local time period. This process is not the focus, and this article will not expand on it due to space limitations.

[0092] When the system is running, the energy storage system charging and discharging power {P BESS-c,t,i,k ,P BESS-d,t,i,k}、Battery pack charging and discharging power {P Btr-c,t,i,k ,P Btr-d,t,i,k} should not take non-zero values ​​at the same time. This is not directly reflected in the previous model, but it can be indirectly proved that it can be automatically realized through the existing constraints. Consider the following linear transformation:

[0093]

[0094] And the linear transformation:

[0095]

[0096] The above-mentioned paired variables can be transformed equivalently, and it is easy to find and These two pairs of new variables are affected by the energy storage system battery pack’s energy F Btr,t,i,k Constraints must be met:

[0097]

[0098] When the initial state is determined, its change process is only related to the In addition, it is not difficult to verify that the primary and secondary optimization objectives f1 and f2 are linearly positively correlated with the elements of the two pairs of new variables. Using the proof by contradiction, assuming that a certain period of time in the optimal solution of the optimization model constitutes or , and takes non-zero values. Then we can always ensure that and Under the condition of no change, reduce the amount or The value of will reduce the total amount of photovoltaic power generation and purchased electricity in the distribution system. That is, the value of the optimization target f1 or f2 will become smaller, which contradicts the optimal solution obtained.

[0099] In demand-side management of building HVAC systems, {P Th-c,t,i,k +ΔP Th-c,t,i,k ,PTh-h,t,i,k +ΔP Th-h,t,i,k The cooling / heating power is also guaranteed not to take non-zero values ​​at the same time. The proof method is similar and will not be repeated here.

[0100] Furthermore, the design for minimum energy consumption in step 4) comprises the following steps:

[0101] In addition to the photovoltaic and energy storage capacity planning described in the previous section, the design of low-energy building distribution systems also includes the spatial layout of the source, grid, load and storage parts of the distribution system, voltage level / type planning, equipment selection and other different contents. At present, there have been a lot of research on building electrical energy-saving design, and the design strategies include but are not limited to: substation at the nearest power center; reducing the number of substations; improving power factor and reducing harmonics; using DC distribution; selecting high-efficiency new energy power generation equipment, transformers / converters, light sources, elevators and energy storage equipment. Engineers strictly implement this strategy regardless of cost to design the building distribution system, and combine the content of the previous section for capacity planning, so as to complete the design plan with the lowest energy consumption. The specific process will not be described in detail in this article.

[0102] Since the technology and equipment used in the design of traditional building power distribution systems are widely used and low in cost, the lowest energy consumption solution reduces the electricity purchase costs incurred during the operation of the building, and even achieves zero external electricity purchase, but it is not necessarily more advantageous in terms of overall economic efficiency. Therefore, it is necessary to optimize the spatial layout and select energy-saving technologies and equipment for use.

[0103] Economically optimal design:

[0104] Due to the restrictions of building use requirements, market supply conditions and other factors, the design details such as spatial layout, energy-saving technology, and the number and type of equipment used in the design of building power distribution systems are always limited. However, as the scale of buildings grows, the number of optional design schemes and the number of design details increase explosively, and it is impractical to verify the impact of design parameter changes under different schemes one by one.

[0105] The energy consumption and economic goals in the capacity planning of building power distribution systems, as well as the constraint equations, are not only related to the optimization variables, but also to the vector ξ formed by the design parameters. Complete equations (2-2)-(2-3) into the following parameter-containing form:

[0106]

[0107] Observe Step 2 of formula (2-5) and assume that its optimal solution is x * , and the corresponding parameter is ξ * Since the extreme points of the linear programming problem must be at the boundary vertices specified by the constraints, the optimal problem is transformed into:

[0108]

[0109] in, is the vector of zero values ​​of the constraint equation g when the optimal solution is obtained. After applying disturbance to the above equation, we can get:

[0110]

[0111] Where γ ξ That is, it is the sensitivity of target f2 to parameter vector ξ under the condition that capacity planning variable x takes the extreme value. Suppose the change in distribution system design parameters after adjustment at the uth position (assuming there are U types in total) is Δξ u . We can then determine whether specific design details need to be adjusted by solving the following economic optimal design problem:

[0112]

[0113] In the formula is the maximum energy consumption allowed for the building (i.e., purchased electricity), v u To determine the 0-1 variable that accepts the adjustment at the uth position, take 1 to judge that the adjustment is accepted. This form of integer programming problem has a mature solution method, which will not be described in detail in this article.

[0114] The beneficial technical effect of the present invention is that: based on the characteristics of each part of the "source-grid-load-storage" in the building power distribution system, the present invention establishes a building energy consumption and cost calculation model, constructs the corresponding optimization problem, and solves the design parameters such as photovoltaic and energy storage capacity through the Benders decomposition method. Because there is room for adjustment in other design details, a cost-to-parameter sensitivity model is constructed to assist in the optimization of relevant parameters. Through the two-step iteration of capacity planning and parameter adjustment, the overall optimal design of the distribution system is achieved. The case results show that the method in this paper is practical and feasible and can achieve the expected results. BRIEF DESCRIPTION OF THE DRAWINGS

[0115] Figure 1 Design a flow chart for the overall power distribution system. DETAILED DESCRIPTION

[0116] In order to enable those skilled in the art to more clearly understand the objectives, technical solutions and advantages of the present invention, the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0117] Reference Figure 1As shown, combined with the contents of the specification, the low-energy building design obtained by the present invention can be divided into several stages: first, model the electrical (i.e., energy consumption) characteristics and economic costs of the design details of each part of the source, grid, load and storage; second, select energy-saving technologies and equipment to solve the Step 1 minimum energy consumption and Step 2 economic optimal capacity planning problems; third, calculate the sensitivity of economic goals to the adjustment of design detail parameters; fourth, solve the economic optimal design problem and determine the adjustment of design details. It should be noted that because the calculation models of the third and fourth steps are at point x * The linear approximation of the neighborhood has errors, so it must be substituted back into the capacity planning problem in the second step for verification after the calculation is completed, and the third and fourth steps must be re-performed to verify whether the design details need further adjustment.

[0118] Example: In order to test the planning method of this paper, the following single-story exhibition hall case is established with reference to typical building structures.

[0119] The envisioned case is located in Hangzhou, and the local long-term weather data is generated based on Meteonorm meteorological software. The building faces east and west, and the top surface areas of the east and west parts are 433m2 and 293m2 respectively. After ignoring the shading of surrounding objects, the PVSyst photovoltaic design software is used to obtain the overview of the building's light resources.

[0120] Regarding the electricity consumption of buildings, this paper simulates and estimates the load power of HVAC, lighting, socket equipment, and electric vehicle charging at different hour intervals throughout the year to maintain comfortable conditions in buildings, referring to relevant standards [2, 3, 7-9]. Various cost information in the distribution system (such as time-of-use electricity prices) is obtained through the Internet.

[0121] Preliminary planning: Nearly zero-energy buildings do not have clear requirements for the comprehensive energy consumption of public buildings. This article takes the annual average energy consumption limit of 55kWh per square meter of residential buildings as a restriction on the design of the exhibition hall.

[0122] Table 1 System settings for different solutions

[0123]

[0124] To compare the effect of the proposed method, four schemes are set as shown in the table above: (a) intelligent temperature control, (b) photovoltaic full load, (c) energy storage full load, and (d) photovoltaic + energy storage full load. The lithium iron phosphate battery is designed to work between 65% and 95% of capacity, and the daily initial power is taken as the average value. Compared with (e) the optimal scheme planned in this paper, the following simulation calculation results are obtained.

[0125] Table 2 Energy consumption and cost of different solutions

[0126]

[0127] Note that the costs in the table have already deducted the cost of charging electric vehicles (including service fees), and the various expenditures in the investment, construction, and operation of the power distribution system have not yet been fully counted. However, the relative levels of the data are sufficient to identify the differences between the plans.

[0128] It is not difficult to see that in scheme (a), without photovoltaic and energy storage, the building energy consumption is already very close to the limit of near-zero energy building. This is because the default building has adopted an enclosure structure with excellent thermal insulation performance and an efficient HVAC system, and the values ​​of the cooling, heating and electricity load requirements of the example have been set according to the standard requirements. Scheme (b) reveals that the large-scale installation of photovoltaics has reduced the purchase of electricity from the power grid, saving a little cost while reducing energy consumption. The results of scheme (c) show that the operation of the energy storage system alone will increase the overall energy consumption, and it is difficult for energy storage users to generate income from Hangzhou's peak and valley electricity prices. Scheme (d) shows that the simultaneous installation of photovoltaic and energy storage systems can further reduce the overall energy consumption, but without proper planning, additional costs will be incurred.

[0129] The planning scheme (e) in this paper appropriately plans the photovoltaic installation area and energy storage capacity to avoid the waste caused by the excessive distributed photovoltaic power generation that cannot be connected to the grid during the period of strong sunlight throughout the year. In this way, the operating cost can be minimized while maintaining the low energy consumption of the building.

[0130] Design adjustment: All the above schemes have implemented demand-side management for building cooling and heating loads, and the energy-saving effect relative to the increase in cost is still unclear. Therefore, in the economic optimal design of the distribution system, it is possible to consider canceling the demand-side management parameter change, simulate the relevant impact and make trade-offs. Reflected in the specific model, that is, the temperature limit in formula (1-17) and θ t,i,k (In winter and summer, the temperature is fixed at 25℃), C in formula (1-20) DR , and the power consumption P in equations (1-18) and (1-19) DR,t,i,k And heat Q DR,t,i,k Needs correction.

[0131] In the exhibition hall scenario, the additional cost after configuring the demand-side management system mainly comes from equipment investment, which is estimated to be 80,000 yuan after considering the replacement. After substituting the adjusted parameters into equations (3-3) and (3-4), it can be estimated that after canceling the demand-side management of the cooling and heating system, the average cost will drop by 90,900 yuan in 40 years. Although this value is not accurate, it is enough to show that it is not economical to configure a cooling and heating load demand-side management system in an exhibition hall with a smaller building scale.

[0132] After updating the parameters, re-solving the Step 2 problem in equation (2-5) yields the following new solution:

[0133] Table 3 System settings, energy consumption and cost of the new solution

[0134]

[0135] It can be seen that after the cancellation of demand-side management, the total cost decreased by 87,100 yuan, which is not much different from the previous estimated result. This shows that the fourth step in the overall process of low-energy building design, the economic optimal design conclusion is relatively credible and can guide the adjustment of design details.

[0136] The specific embodiments described herein are merely illustrative of the principles and effects of the present invention and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A method for planning a low-energy building distribution system considering source, grid, load and storage characteristics, characterized by: The steps include: 1) Model the electrical characteristics and economic costs of the design details of each part of the source, grid, load and storage respectively; the modeling process includes establishing a building power system model, a building distribution network model, a building power load model and a building energy storage system model; wherein, the establishment of the building power system model includes photovoltaic power generation and purchased electricity, and the establishment of the photovoltaic power generation model includes the following steps: Considering the photovoltaic installation conditions at different locations of the building, the average annual power generation per unit area of ​​the components, and the cost per unit area, the component type for each area is selected; assuming that the building contains M power generation areas, and the construction area of ​​each part is A. j satisfy: In the formula, is the upper limit of the installation area and available area of ​​the jth block. If the whole year is divided into N periods, the upper limit of the photovoltaic system power generation in the kth period in the i-th input scenario can be calculated using meteorological data: In the formula is the power generation per unit area of ​​the photovoltaic module in the jth block area and the kth time period in the i-th input scenario; the maximum power generation capacity of the photovoltaic system decays year by year, and the function of the relative initial capacity ratio changing with time is ρ PV (t), and the PV modules reach their service life T PV If it is replaced after the year, the upper limit of power generation in the corresponding scenario and time period in year t is: where mod is the remainder; therefore, the photovoltaic system power generation P in the kth period under the i-th scenario in the t-th year is PV，t，i，k satisfy: Assume that the fixed investment cost per unit area, annual average operation and maintenance cost, and recovery cost of the jth region are c PVinv，j 、c PVopr,j and c PVrec，j ; then the fixed investment cost C PVinv , Operation and maintenance cost C PVopr and recovery cost C PVrec satisfy: Where r dis is the annual discount rate, and the useful life of the building is T B , from which the total investment of photovoltaic power generation system in this year is: The model establishment of purchased electricity includes the following steps: For the purchased power P in the kth period under the i-th scenario in the tth year, Grid,t,i,k Only the lower limit is applied: P Grid，t，i,k ≥0 (1-7)； Ignoring the impact of rising building electricity prices, only considering the short-term peak-valley differences in some regions, the electricity price for the kth period in the first year after the building is completed and put into use is c Grid,k , the cost of purchasing electricity inside and outside the building during its service life can be estimated as: 2) Further optimize the model and solution method, select energy-saving technologies and equipment, and solve the problem of minimum energy consumption and economically optimal capacity planning; 3) Calculate the sensitivity of economic objectives to the adjustment of design detail parameters; 4) Solve the economic optimal design problem and determine the adjustment of design details; The establishment of the building power distribution network model includes the following steps: According to the topological connection position, the distribution network is divided into three parts: near source, near load, and near storage. The capacity of the near source part is proportional to the corresponding power source, and is composed of two parts connected to photovoltaic and connected to the external network. The capacity of the near load part is determined by the load and can be calculated according to the required coefficient method. The capacity of the near storage part is proportional to the energy storage. After determining the selection scheme, the direct cost can be calculated based on the total capacity of the distribution transformer equipment. The direct cost of the near source part is: C Net / Gen =c Net / PV E PVsum +C Net / Grid (1-9); Among them, c Net / PV is the cost of inverter and other equipment corresponding to unit photovoltaic capacity, C Net / Grid is the direct cost of the grid-side transformer / converter, E PVsum is the total installed capacity of photovoltaic power generation, and the installed capacity per unit area of ​​the jth region is e PV,j , then: In addition, the direct costs of the near-storage part are: Among them, c Net / BESS is the cost of converter and other equipment corresponding to unit energy storage capacity, E BESS is the energy storage capacity, Assume C is the upper capacity limit. Net / Load is the direct cost of the near-load part, then the total direct cost of the distribution network is: C Net =C Net / Gen +C Net / Load +C Net / BESS (1-12); After the distribution network equipment selection scheme is determined, the rated load state of each device is selected to calculate the power conversion rate of the three parts of the distribution network near the source, near the load, and near the storage. Based on this, the network loss level under different working conditions of the distribution network is evaluated; record the charging and discharging power of the energy storage system in the kth period of the i-th specific scenario in the t-th year as P respectively. BESS-c,t,i,k and P BESS-d，t，i，k , the load power is P Load，t，i,k ; Then the power balance equation of the distribution network is: Among them, η PV2Net and η Grid2Net are the conversion rates of photovoltaic power and external power grid power to building distribution network, η Net2BESS and η BESS2Net are the power conversion rates from distribution network to energy storage and from energy storage to distribution network, η Net2Load is the power conversion rate from distribution network to load, which are all less than 1; according to the principle of energy conservation and ignoring the electromagnetic waves radiated by the distribution network, the heating power of the distribution network can be obtained as:

2. The method for planning a low-energy building distribution system considering source, grid, load and storage characteristics according to claim 1 is characterized in that: The establishment of the building power load model includes public load, user load and demand side response, wherein the establishment of the public load and user load model includes the following steps: The direct cost of cooling and heating loads for building fresh air and air conditioning equipment is recorded as C Thm ; Through the energy consumption simulation software, the power consumption of cooling / heating load in the kth period in the i-th specific scenario in the t-th year can be solved as P Th-c,t，i，k / P Th-h,t,i，k ; At this time, the power load in the building and the heating power of the energy storage system are Q Load，t，i，k and Q BESS,t,i，k , then the total heating power of the power distribution system satisfies: For building power and public lighting loads, there are R categories, of which the rth direct cost is recorded as C Elc，r The power change of this type of load is related to work and vacation. By setting the usage frequency, or according to the usage of similar loads in similar buildings, combined with the construction scale to estimate the power consumption, the power consumption of the rth load in the kth period in the i-th specific scenario in the t-th year is recorded as P Elc,t,i，k，r , the heating power is Q Elc，t，i，k，r ; For user-related loads, including socket loads and electric vehicle charging loads, their power conditions can be estimated by setting the usage frequency, or based on the usage of similar loads in similar buildings, combined with the construction scale estimation, and the power consumption in the kth period in the i-th specific scenario in the t-th year is recorded as P Plg，t，i，k , and convert the indoor equipment heating power Q according to the proportion Pig，t，i，k When studying the total energy consumption of a building, this part of energy consumption should be excluded, and the unit price of the power supply fee charged by the owner to the user is c Plg , then the sum of the owner's electricity sales revenue in all scenarios is:

3. The method for planning a low-energy building distribution system considering source, grid, load and storage characteristics according to claim 2 is characterized in that: The model establishment of the demand side response includes the following steps: Assume that the HVAC system energy consumption simulation software solves the average building temperature in the kth period in the i-th specific scenario in the t-th year as Θ t，i，k , the upper and lower limits of comfort are required to be and Θ t,i，k , by determining the power change ΔP after the demand side responds Thm，t，i，k，r , the average temperature change ΔΘ can be solved by the approximate linear deterministic model t，i，k , specifically meet: Among them, α t，i，k , β t，i，k and γ t，i，k is the sensitivity coefficient of the system temperature at a specific moment, which is related to the environment, enclosure and HVAC system parameters and operation mode. Heat in the building configured for HVAC system energy consumption simulation, ΔP Th-c，t，i，k / ΔP Th-h,t，i，k is the cooling / heating electricity load increment; for the convenience of calculation, the impact of the temperature change at the end of the previous day on the next day is ignored, and the corresponding time coefficient α t，i，k The value is 0; The implementation of demand-side management inevitably requires the configuration of measurement sensors and automatic control devices in the building, as well as energy management servers; therefore, the direct cost C brought by these devices must also be calculated. DR 、Electric power P DR，t，i，k And heating power Q DR，t，i，k , thus, the total power consumed by the load is: In addition, the total heating power of the supplementary load is expressed as follows: In addition, the total direct cost of the building's electrical load is:

4. The method for planning a low-energy building distribution system considering source, grid, load and storage characteristics according to claim 3 is characterized in that: The establishment of the building energy storage system model includes the following steps: First, we should pay attention to the energy consumption of the energy storage system. The energy consumption of the battery energy storage system in the building includes two parts: the battery pack and the heat dissipation equipment. Therefore, the characteristics of the two parts should be examined separately in the kth period under the i-th scenario in the t-th year. Suppose the energy retained by the battery pack at this time is P Btr，i,j,k The charging and discharging powers of the battery pack are P Btr-c,t，i，k and P Btr-d，t，i，k , then the energy F retained in the k+1th time period after the unit time interval Δt can be obtained Btr，i,j,k+1 satisfy: where η Btr-s is the battery self-discharge rate per unit time interval, η Btr-c and η Btr-d They are the charging and discharging efficiency of the battery pack, and their values ​​are both less than 1; set the upper and lower limits of the ratio of the retained energy to the initial capacity and λ Btr ; In actual operation, it is difficult to predict building electricity consumption on a long time scale with a sampling interval of hours, so energy storage charging and discharging is often optimized with one day as the cycle. The last two rows of constraints ensure that the battery energy at the beginning and end of each day is consistent with H Btr ; According to the principle of energy conservation, the heating power of the battery pack in the kth period under the i-th scenario in the t-th year is: Secondly, let the inverse of the energy efficiency index of the heat dissipation equipment be κ Bcl , the power consumption is: P Bcl，t，i，k =k Bcl ·Q Btr，t，i,k (1-23); It still releases heat to the overall environment inside and around the building, and the heating power is: Q Bcl，t，i，k =P Bcl，t，i，k (1-24); At this point, the overall power consumption equation of the energy storage system can be obtained: And the total heating power of the energy storage system: Q BESS，t，i，k =Q Btr，t，i，k +Q Bcl，t，i，k (1-26); Economic model of energy storage system: Assume that the fixed investment cost per unit capacity of energy storage, the annual average operation and maintenance cost, and the recovery cost are C BESSinv 、c BESSopr and c BESSrec ; If the service life of the energy storage system is T BESS Years, then the fixed investment cost C BESSinv , Operation and maintenance cost C BESSopr and recovery cost C BEESrec satisfy: The total investment in the energy storage system during the service life of the building is:

5. The method for planning a low-energy building power distribution system considering source, grid, load and storage characteristics according to claim 4 is characterized in that: The step 2) comprises the following steps: The photovoltaic installation area, energy storage capacity, daily initial energy storage capacity, and electrical / thermal / temperature operating status of the distribution system in the planning scope in equations (1-1) to (1-26) are recorded as x, and the main optimization objective of the capacity planning problem is: The secondary objectives are: Formulas (1-1) to (1-26) are uniformly marked as constraints: g(x)≤0 (2-3); Therefore, the optimization model can be written as: The model contains multiple layers of optimization that are difficult to solve directly, but can be decomposed into two independent mixed integer linear programming problems, which are solved sequentially:

6. The method for planning a low-energy building distribution system considering source, grid, load and storage characteristics according to claim 5 is characterized in that: Solving the economic optimal design problem in step 4) specifically includes the following steps: The energy consumption and economic goals in the capacity planning of building power distribution systems, as well as the constraint equations, are not only related to the optimization variables, but also to the vector ξ composed of design parameters; Equations (2-2)-(2-3) are completed into the following parameter-containing form: Observe Step 2 of equation (2-5), assume that its optimal solution is x, and the corresponding parameter is ξ * , since the extreme points of the linear programming problem must be at the boundary vertices specified by the constraints, the optimal problem is transformed into: in, is the vector of zero values ​​of the constraint equation g when taking the optimal solution. After applying disturbance to the above equation, we can get: Where γ ξ That is, the sensitivity of target f2 to parameter vector ξ under the condition that capacity planning variable x takes the extreme value; let the change of distribution system design parameter after adjustment at the uth position be Δξ u ; We can determine whether specific design details need to be adjusted by solving the following economic optimal design problem: In the formula is the maximum energy consumption allowed for the building (i.e., purchased electricity), v u A 0-1 variable to determine whether the adjustment at the uth position is accepted. A value of 1 indicates that the adjustment is accepted.

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