A novel comprehensive energy management model for multi‐microgrids considering ancillary services

Abstract

This article proposes a novel comprehensive multi-layer power management system (PMS) along with its smart distribution network (SDN) constraints as bi-level optimization to address the participation of multi-microgrids (MMGs) in day-ahead energy and ancillary services markets. In the first layer of the proposed model, optimal programming of MMGconnected SDN is considered, in which Microgrids (MGs) participation in the markets is performed to bidirectionally coordinate sources and active loads along with the operator of MGs. In the second layer, the bidirectional coordination of operators of MGs and SDN, that is PMS, is executed in which energy loss, voltage security, and expected energy not-supplied (EENS) are minimized as weighted sum functions. The problem of the difference between costs and revenues of MGs in markets is minimized subject to constraints of linearized AC-power flow, reliability, security, and flexibility of the MGs. To obtain a singlelevel model, the Karush-Kuhn-Tucker method is applied, and a hybrid stochastic-robust programming is implemented to model uncertainties associated with the load, renewable power, energy price, mobile storage energy demand, and network equipment accessibility. The contributions of this paper include the simultaneous modelling of several economic indicators, multi-layer energy management modelling, and stochastic mixed modelling of uncertainties. The efficiency of this method is validated by simultaneously evaluating the optimum condition of technical and economic indices of several SDNs and MGs. Flexibility of 0.022 MW is obtained for the proposed scheme, which is close to zero (100% flexibility). The voltage security index is increased to 22 by the mentioned scheme, which is close to its normal value, that is, 24. The voltage deviation is below 0.07 p.u. Energy losses are reduced by about 30% compared with that in power flow studies, and the EENS reaches roughly 3 MWh, that is, close to zero (100% reliability).

Stochastic Coordinated Management of Electrical–Gas–Thermal Networks in Flexible Energy Hubs Considering Day-Ahead Energy and Ancillary Markets

“…In this section, the KKT method is used to extract a single-level integrated model solvable by conventional algorithms [58]. The basic requirement of this method is the convexity of the lower-level problem.…”

“…The single-level problem then includes the upper-level problem model Equations ( 1)-( 14) and constraints of the KKT [58].…”

“…Relations ( 54)-( 58) express the KKT model for the lower-level problem ( 15)- (33). Constraint ( 54) is obtained from the equality of the Lagrangian function derivative to the lower-level formulation variables with ( ∂L ∂y = 0) [58]. Constraint ( 55) is also obtained from ∂L ∂ρ = 0, which includes equal relations ( 16)-( 21), (23), and ( 31) in lower-level problems.…”

“…Constraint ( 55) is also obtained from ∂L ∂ρ = 0, which includes equal relations ( 16)-( 21), (23), and ( 31) in lower-level problems. It is worth mentioning that ∂L ∂µ = 0 has two conditions which are known as the first and second conditions of KKT [58]. In the first condition of KKT, the unequal conditions ( 22), ( 24)-( 30), (32), and (33) are derived similarly to Constraint (56), from ∂L ∂µ = 0.…”

“…In the first condition of KKT, the unequal conditions ( 22), ( 24)-( 30), (32), and (33) are derived similarly to Constraint (56), from ∂L ∂µ = 0. In the second condition of KKT, the equality of the multiplication of the Lagrangian coefficient µ and inequality constraint boundary equation, C.y − c, to zero is derived from ∂L ∂µ = 0, which has been presented in Constraint (57) [58]. Finally, the range of Lagrange coefficients changes has been considered in Constraint (58).…”