Multi-objective-based optimal transmission switching and demand response for managing congestion in hybrid power systems

ABSTRACT

This paper proposes a novel congestion management (CM) approach by using the optimal transmission switching (OTS) and demand response (DR) for a system with conventional thermal generators and renewable energy sources (RESs). In this paper, wind and solar PV units are considered as the RESs. The stochastic behavior of wind and solar PV powers are modeled by using the appropriate probability density functions (PDFs). The proposed CM methodology simultaneously optimizes the generation dispatch, demand response, and also the network topology of the power system. The OTS identifies the branches that should be taken out of service by significantly reducing the operating cost of the system while respecting the system security. Here, the total operating cost minimization/social welfare maximization and system losses minimization are considered as the objectives to be optimized. The proposed CM problem is solved using the multi-objective Jaya algorithm and it is used to determine a set of Pareto-optimal solutions. The Jaya algorithm is simple and it does not have any algorithmic-specific parameters to be tuned. This aspect reduces the designer’s effort in tuning the parameters to arrive at the optimum objective function value. A fuzzy logic-based approach is used to identify the best compromise solution. The effectiveness of the proposed CM approach is examined on modified IEEE 30 and practical Indian 75 bus test systems. The obtained simulation results are analyzed and they show the effectiveness of the proposed approach.

KEYWORDS:

• Congestion management
• demand response
• transmission switching
• wind power
• solar PV power
• multi-objective optimization

Nomenclature

$C_{Gi}$	=	Generation cost curve of generator i.
$\lambda$	=	Loading margin.
$P_{wj}$	=	Scheduled wind power generation.
$d_j$	=	Direct cost coefficient of wind energy generator.
$P_{Sk}$	=	Scheduled solar PV power generation.
$e_k$	=	Direct cost coefficient of solar PV generator.
$P_{shd,l}$	=	Amount of load shed/demand response provided by lth load demand.
$x_k$, $y_k$, $z_k$	=	Demand response coefficients for lth load demand.
$e_l^{\prime }$, $f_l^{\prime }$, $g_l^{\prime }$	=	Demand coefficients of lth load demand.
$P_{Dev,i}$	=	Power deviation from the scheduled wind and solar PV powers.
$\underset{\_}{P_{Dk}}$	=	Lower bound on the decreased or curtailed level of power consumed.
$P_{rj}$	=	Rated/maximum power output from the WEG.
$P_{Sk}^{max}$	=	Maximum power output from the solar PV unit.
$x_i^{\prime }$, $y_i^{\prime }$, $z_i^{\prime }$	=	Cost coefficients of thermal generators for providing the deviated power.
P	=	Size of initial population.
ξ	=	Crowding distance.
$P_{Gi}$, $Q_{Gi}$	=	Active and reactive power generations of ith generating unit.
$P_{Di}$, $Q_{Di}$	=	Active and reactive power demands of ith load bus.
$G_{ij}$, $B_{ij}$	=	Transfer conductance and susceptance between buses i and j.
${\mathbf{N}}_{\mathbf{S}\mathbf{A}}$	=	Number of switching actions.
$N_G$	=	Number of conventional thermal generators.
$N_W$	=	Number of wind energy generators (WEGs).
$N_S$	=	Number of solar PV units.
$N_D$	=	Number of loads/demands.
$R_c$	=	Certain irradiation point set as 250 W/m2.
$G_{std}$	=	Standard solar irradiation set as 1000 W/m2.
$P_S^r$	=	Rated power output of solar PV generator.
$a_i$, $b_i$, $c_i$	=	Fuel cost coefficients of thermal generator.
n	=	Total number of buses in the system.
G	=	Solar irradiance.
$V_i$, ${\delta }_i$	=	Voltage magnitude and phase angle at bus i.
$S_{ij}$	=	Power flow/MVA flow through a line connecting between the buses i and j.
$S_{ij}^{max}$	=	Stability/thermal limit of line connecting between the buses i and j

Conflicts of Interest

The author declares no conflict of interest.

Additional information

Funding

This research has been carried out based on the support of Woosong University’s academic research funding (2019–2020).