基于matlab多时间尺度滚动优化的多能源微网双层调度模型.rar

two-stage framework based on transactive control, to achieve the optimal energy provision among

interconnected multi-energy systems (MESs). At the lower level, each MES autonomously determines

the optimal setpoints of its controllable assets by solving a cost minimization problem, in which

rolling horizon optimization is adopted to deal with the load and renewable energies’ stochastic

features. A technique is further implemented for optimization model convexification by relaxing

storages’ complementarity constraints, and its mathematical proof verifies the exactness of the

relaxation. At the upper level, a coordinator is responsible for minimizing total costs of interconnected

MESs while preventing transformer overloading. This collaborative problem is solved iteratively

in a proposed two-stage transactive control framework that is compatible with operational time

requirement while retaining scalability, information privacy and operation authority of each MES.

significant attention for their ability to improve comprehensive energy efficiency, as well as to benefit

system economy and environment [

energy vectors and networks, as well as handle the uncertain and volatile generation outputs of

renewable energy sources (RES) [

,

4

]. Besides, owing to the energy complementarity, MES has the

untapped potentials of jointly minimizing the operational cost [

5

], enhancing the overall operational

efficiency and to increasing system flexibility [6].

7

], recent years have witnessed a

among multiple interconnected MESs (IMESs) [

6

]. Collaboration methods of interconnected subsystems

generally fall into centralized and distributed mechanisms. The centralized models are established

solutions, it is capable of protecting crucial information of individual entities [

12

(ADMM) algorithm [

13

,

14

]. For example, an incentive mechanism using the Nash bargaining solution is

proposed to encourage interaction and sharing among interconnected microgrids in [

15

]. A hierarchical

13

infeed of RES. While these methods address the issue of scalability, the coordination signals used

in these methods, e.g., the Lagrange multipliers, do not provide explicit market information in

3

which makes it unpractical in respect to communication latency, throughput, and distortion.

Considering the uncertainties and fluctuations of RES output and load demand profile, a collaboration

framework for intra-day or even real-time operation is highly demanded, to better integrate high

penetration of RES and to improve overall energy efficiency. Therefore, much research is needed to

increase the convergence speed [

17

]. While an adaptive scheme has been proposed in [

23

] for power

dispatching among networked microgrids in this aspect, it is not applicable to optimizations with

intertemporal constraints.

concerned with constraints that prevent simultaneous charging and discharging of energy storages.

solution. However, the optimization problem has to be solved twice in this case. Some preliminary

work regarding this issue has been made in our previous paper [

30

to provide proof mathematically and through simulation.

for the collaborative autonomous optimization of multiple interconnected MESs. To be detailed,

“autonomous” implies that each self-governed MES is authorized to conduct optimization to supply

local demand independently; “collaborative” confirms that the IMESs’ benefit as a whole is considered

and obtained jointly; finally, the proposed “two-stage TC framework” guarantees that the collaboration

proceeds in a distributed and scalable manner with a fast convergence speed.

A novel two-stage transactive control framework is further established to reduce the required

iterations so that it applies to intra-day rolling optimizations or even real-time optimizations.

Section 3 then develops the detailed rolling optimization model for each autonomous MES. The

collaborative optimization of the IMESs is formulated in Section 4, together with the proposed

two-stage bi-level TC framework. The proposed method is tested in Section 5. A discussion of

this paper is presented in Sections 6 and 7 finally concludes the paper.

the optimization framework. The assumptions made are also included in this section.

2.1.1. MES Operator

service companies that manage several types of energy concurrently. Therefore, this paper assumes

IMESs and the utility grid, by strategically responding to dispatching signals.

based on the TC mechanism for energy management of IMESs, as shown in Figure 1. In the

proposed framework, each MES optimizes to minimize its operational cost autonomously, and

a collaborative optimization. In this way, issues can be addressed effectively that are associated with

denote electricity consumed by boiler;

,

are efficiencies of the furnace and boiler, respectively;

and

represent the

denote the fixed electric load and thermal load, respectively.

paper. Namely, at time

, each MES seeks to autonomously minimize its expected cost across the

remaining periods based on forecasts of local RES output and load demand. However, only decision of

35

in the next time slot since refreshed forecast data will be provided.

and the gas purchasing cost,

1. The capacity constraint of electricity:

2. The electric capacity limits of CHP, furnace, and boiler:

4. The maximum charging/discharging power constraints:

where

denote the maximum charging and discharging power of the EES, and

,

6. Constraints associated with shiftable loads [36]:

, (14)

where

denote the total shiftable electric and heat load within the scheduling day,

7.