1、 Abstract In order to coordinate and control all kinds of distributed energy in distribution network more effectively and adapt to the core concept of active distribution network,an active distribution network transactional energy minimization method based on market-based control is proposed.Firstly
2、,combining the idea of market-based control with the characteristics of various distributed energy sources in the active distribution network,a market control-based distribution network transactional energy minimization method is proposed.Then,considering the uncertainty factors of renewable energy
3、consumption and active load and the differences between different subjects in distribution network,energy bidding models of distributed energy,adjustable load and energy storage system are established respectively.Moreover,a distribution network energy trading model is established,which realizes the
4、 optimization of distribution network energy and the balance of power supply and demand.Finally,an example is given to verify the effectiveness of the energy minimization method in blocking management of distribution network,increasing the consumption rate of renewable energy and economy.Manuscript
5、received July 15,2008.This work was supported by Yunnan power grid co.,LTD.Electric power science research institute.Research Program:Ruili distribution network automation overall planning and construction plan(YNKJXM20170743).Zou Jingxi(1989),Female,Master,engineer.The main research direction is sm
6、art grid and intelligent power distribution technology.(E-mail:).Qin Han(1992),Male,Master,postgraduate,corresponding author of this article,major research direction is active distribution network,grid information physical system(E-mail:;fax:34204681;phone:17621154057).Liu Dong(1968),Male,Ph.D.,Rese
7、archer,Ph.D.Supervisor,New Century Excellent Talent of the Ministry of Education,the main research direction is the information physics fusion system of smart grid and power grid,(E-mail:liudongieee.org).Cao Min(1961),Male,Professor-level senior engineer,senior technical expert of China Southern Pow
8、er Grid.His main research interests are electric energy metering,smart grid,and intelligent power distribution(E-mail:).Li Wei(1985),Male,senior engineer.The main research direction is smart grid and power distribution automation(E-mail:).Index Terms Market-based control;Trading style;Energy optimiz
9、ation;Distributed energy;Balance of power supply and demand.I.INTRODUCTION N order to solve the problem of wide access of distributed power,active distribution network technology has developed rapidly in recent years1.Distribution network energy management system(DEMS)plays an important role in ensu
10、ring the safe and efficient operation of distribution network.DEMS mainly follow the traditional practice of large power grid and adopt centralized optimization method.When the distribution network structure is complex,traditional methods cannot solve the above problems,and modern optimization techn
11、iques,such as genetic algorithm2-3 and particle swarm optimization algorithm4,are often needed to optimize the energy of the distribution network.There are also some literatures that reduce the solution complexity by linearizing high order functions and introducing heuristic rules5.Although this met
12、hod has the advantages of information concentration and easy to get the optimal solution,with the development of the energy Internet concept,the following common defects gradually emerge6.The first is the difficulty of adapting to the user-centric concept of the energy Internet7.The main performance
13、 is:1）it is difficult to effectively implement privacy protection.All kinds of distributed energy sources need to provide their own important parameters,such as dynamic models,such as cost curve,output curve,operating characteristics 5.2)In the future,more emphasis will be placed on the personalized
14、 expression of electricity strategy.The future more emphasis on electricity strategy personalized expression,human power consumption behavior change and ambiguity,and centralized optimization requires a relatively stable and the power of the modeling strategy can lead to poor user experience6-7.Seco
15、ndly,the centralized architecture is difficult to adapt to Distribution network transactional energy minimization method based on market-based control ZOU Jingxi1,QIN Han2*,LIU Dong2,CAO Min1,Li Wei1 1.Yunnan power grid co.,LTD.Electric power science research institute 2.Shanghai Jiao Tong Universit
16、y I 2018 China International Conference on Electricity Distribution Tianjin,17-19 Sep.2018CICED2018 Paper No.201805280000378 Page1/7 2972 the core features of the open and extensible energy Internet8.This displays in:1)different types of information interaction between DER and DEMS are diverse and c
17、omplex,and the future demand side need more flexibility,but the centralized optimization is difficult to adapt to this kind of open,extension exhibition needs;2)Plug-and-play features are not supported,but they are an inevitable requirement for future mass user terminals.3)As the size of distributio
18、n network increases,the calculation amount of centralized optimization increases rapidly,and the cost of centralized control for a large number of DER is too high9.In order to solve the above problems,a distributed energy optimization method based on market control is proposed.MBC is a kind of distr
19、ibuted control method applying market equilibrium mechanism10-12.It has been used in the computer field for a long time to solve the optimal allocation problem of limited computing resources.In recent years,MBC,also known as transaction control,has been used for the coordination control of large-sca
20、le DER.The main characteristic of the method in this paper is to optimize the energy of distribution network.The energy distribution network is optimized with DER distributed decision based on the balance of power supply and demand for maintenance13-15.Compared with existing studies,the main differe
21、nces and contributions of this paper include:1)The existing researches mainly focus on technical targets,such as blocking management and supply balance of regional distribution network16.2)The key to the MBC approach is to construct bidding strategies for distributed resources.Previous studies have
22、focused on temperature control load,and this paper systematically studied various DER heuristic bidding strategies17.II.MARKET-BASED CONTROL A.The solution method of optimization problem Set in the n(n=1,2,.n)optimization period,the power of i(i=1,2,.I)of the power supply in the distribution network
23、 is,the power of the load is,the cost function of the power supply i is,and the cost function of the load j is je.Considering the total power consumption income of all loads in the distribution network minus the total power generation cost of all power sources as the total social welfare of the micr
24、ogrid,the problem of energy optimization of the distribution network can be regarded as the problem of maximizing social welfare:N I Jnnn=1 i=1 j=1IJGi n Lj ni=1 j=1nn12min-s.t.=i Gi i LjGi Gi GiLj Lj LjGu Gi Gi Gic P e PPPP P PP P Pg P P P，min，max，min，max，（）（）（,.,N1 2 N0 0Lv j j jg P P PL，L，L，）（,.,
25、）(1)Where:，and，are the upper and lower limits of the power supply I respectively;，and，are the upper and lower limits of load j,respectively.and are the cross-cycle constraints of power supply and load respectively,such as the limit of climbing rate of power supply and the total demand of adjustable
26、load,etc.In the centralized optimization,the problem can be solved by solving equation(1).In order to realize distributed optimization,the Lagrangian relaxation method is adopted for the problems shown in equation(1)to relax all the equality constraints,then.N I Jnnn=1 i=1 j=1N J In Lj n Gi nn=1 j=1
27、 i=1nn12min-+-s.t.i Gi i LjGi Gi GiLj Lj LjGu Gi Gic P e PPPP P PP P Pg P P，min，max，min，max，（）（）（）（,.N1 2 N0 0GiLv j j jPg P P P，L，L，L，,）（,.,）(2)Where:n are the relaxation factor corresponding to the n optimization period;The problem shown in equation(2)is decoupled to each DER.For power supplyi:Nn=
28、1n1 2 Nmin-s.t.0i Gi n Gi n Gi nGi Gi GiGu Gi Gi Gic P PP P Pg P P P，min，max，（）（,.,）(3)Where:iU are the set of cross-cycle constraints for power i；Gi n，are the relaxation factor of power supply icorresponding to the Nth optimization cycle.For load j；there is：2018 China International Conference on El
29、ectricity Distribution Tianjin,17-19 Sep.2018CICED2018 Paper No.201805280000378 Page2/7 2973 NL L Lj nn=1n1 2 Nmin-+s.t.0j j n j nLj Lj LjLv j j je P PP P Pg P P P，min，maxL，L，L，（,.,）(4)Where:iV are the set of cross-cycle constraints for load i；Lj n，are the relaxation factor of power supply j corresp
30、onding to the Nth optimization cycle.In this way,the maximization of social welfare as shown in equation(2)is transformed into the maximization of each DERs pursuit of his own interests.The problems shown in equations(3)and(4)can be further decoupled into each optimization cycle:jnnmin-c-min-e+s.t.i
31、 Gi Gi GiLj Lj LjGi Gi GiLj Lj LjPPPPP P PP P P，min，max，min，max（）（）(5)Assuming that is convex and continuously differentiable,the problem shown in equation(5)is convex optimization problem18.Can be derived：ax-1i1n2 ax=dc-=ddc ddc+=dGiGiGi Gii GiP i Gi GiGii GiGi Gi GiGii GiP i Gi GiGisPPPPPPP P PPPP
32、PP，min，m，min，min，max，m（）（）（）（）(6)Where:1i and2i are lagrangian multiplier；-1is are the inverse supply function of power i.Similarly,assuming that je is concave and continuously differentiable,the following equation(5)can be obtained:-1ij3jnj2=de+=de de-=LjLjLj LjLjP j Lj GiLjLjLj Lj LjLjLjP j Lj LjL
33、jdPPPPPPP P PPPPPP，min，max，min，min，max，max（）（）（）（）(7)Where:3i and4i are lagrangian multiplier；-1id are the inverse supply function of load j.To realize power balance constraints,is located in the micro network to establish a virtual market,all DER inverse supply or inverse demand function according
34、to the above tender,then empty dont get close to market points out of the total electricity source for standard curve-1iisand load the total bidding curve is-1ijd.If the above general tender curves are strictly monotonous and intersect,the intersection point is unique.At the intersection,supply and
35、demand are balanced and the following market equilibrium conditions are met.Described in this way,(1)energy of conventional distribution network optimization problem is equivalent into each DER distributed decision problem,and in each optimization cycle and only bid and clearing a complete solution.
36、B.Operation process The detailed operation process of MBC distributed network energy optimization method is shown below:At the beginning of each control cycle,(1)All units in the distribution network submit bidding curves to the market;(2)The regional distribution network aggregates all bidding curv
37、es,clears the market and releases the clearing price.(3)Each unit in the distribution network responds to the clearing price and realizes local control.As shown in the figure below.Distribution network schedulingDistribution network contact line agentVirtual marketDG agent Load agentEnergy storage a
38、gentExecute once per cyclePoint-to-point communication Broadcast communication Price signal,load commandBidding curveBidding curveBidding curveBidding curvePrice Price Price Market clearing Control Control Control Fig.1.Operation flow chart The system performs periodic control during normal operatio
39、n.During each control cycle,each agent submits the bidding curve,then the DEMS make the market clearing,and finally the DER agents respond to the clearing price and implement local control.In addition,distribution network dispatching can issue power price signals or direct load cutting orders to dis
40、tribution network through the contact line agent according to the operation needs.In this case,the DEMS run as an event trigger and immediately remarket and publish the price,increasing the system response speed.Information such as the users bidding policy and the DER feature resides only in the age
41、nt and is not uploaded to the DEMS.The communication 2018 China International Conference on Electricity Distribution Tianjin,17-19 Sep.2018CICED2018 Paper No.201805280000378 Page3/7 2974 between each agent and the DEMS is only bidding information and price information,which fully blocks the differen
42、ces between different DER and protects user privacy.In addition,the control signal(price)is broadcast,which greatly simplifies the downward control.III.ESTABLISHMENT OF BIDDING MODEL Set the total optimization duration to H(unit:h),divided into N optimization periods,and the optimization period toT=
43、H/N(unit:h).Set the price variable of the virtual market to be p,the lowest price allowed to be=0,and the highest price to be.A.Diesel generator This paper takes diesel generator as the representative of controllable distributed power supply in distribution network.If only the fuel cost is taken int
44、o account,the operating cost of diesel generator DGC can be fitted as a quadratic function of generator power DGP19.2DG DG fuel 0 DG 1 DG 2=+C P p a P a P a（）（）(8)Where:fuelp Is diesel price;0a,1a,2a are constant coefficients.As can be seen from the previous section,diesel generators are bidding at
45、their marginal cost within the upper and lower limits of power:-1DG DG fuel 0 DG 1DG DG max=2+s P p a P aPP，（）（）0(9)Where:DG maxP，Is the maximum output of diesel generators.B.Renewable energy sources In order to achieve the goal of environmental friendly control,the principle of complete absorption
46、of renewable energy sources such as stroke and light in micro grid should be adopted20.Assuming that REP is the current renewable energy output prediction of the optimization cycle,the bidding curve of the renewable energy constructed in this paper is as follows:RE RE max=-s p P p p（）0(10)C.Base loa
47、d This paper divides load into base load and transferable load.The charge has no demand elasticity.If LBP is the pre-measured value of base load in the pre-optimization cycle,the bidding curve constructed in this paper is as follows:LB LB max=s p P p p（）0(11)D.Bidding strategy with adjustable load T
48、he translatable load has a certain degree of freedom in power consumption time.According to its electrical continuity,it can be divided into two types:medium and non-medium.If each evaluation conforms to the individual bidding,it will easily lead to new spikes.therefore,this paper adopts the method
49、of load aggregation agent to bid.First,several time intervals are set up in advance by analyzing the electricity characteristics of various types of transferable loads.Set an LA for each time interval.Each transferable load is registered to a LA according to its own electrical characteristics.The lo
50、ad aggregation unit LA is required to calculate the highest,lowest,and average bid prices,as well as the maximum,minimum,and average power requirements on a rolling basis in each optimization cycle.Assuming that the current optimization cycle is n,LA predicts the average LA,pavg and standard deviati
