In last few years, distribution network planning has attracted attention of power systems planners because it involves a number of objectives to be simultaneously optimized. The aim of distribution network planners is to develop different approaches to minimize energy loss and to maintain power quality (PQ) of a network. The distribution network planning can be done in two ways: (i) single-stage planning and (ii) multi-stage planning. In single stage planning, whole planning is performed at one step by considering discount factor for a planning horizon. In the case of multi-stage planning, the planning horizon is divided into multiple steps and the planning is performed for each step. There are some state-of-the-art review papers reported on distribution network planning [1-6].
Among these, the papers [1-3] are on review of the passive distribution network (PDN) planning. In PDN, the power flow is unidirectional. The inclusion of distributed generation (DG) in distribution network, converts the PDN to active distribution network (ADN). The works reported on ADN planning and/or PDN planning till 2015 have been reviewed in [4- 6]. The different objectives and decision/optimizing variables used in the planning of PDN and ADN are shown in Table 1.1. The differences between traditional PDN and ADN are categorically shown in [6]. Some typical characteristics of ADN planning, which make it different to the traditional PDN planning are given below.
The designed network topology/structure is to be flexible and it can automatically be reconfigured to a different structure, unlike PDN.
The designed network should reliably work with high penetration of DG.
The designed network should reliably work with uncertainties of the generation of renewable energy sources, such as photovoltaic (PV), wind turbine (WT) etc.
The operation of the designed network should be automated unlike manual operation of PDN.
Table 1.1: The planning cost and decision/optimizing variables for PDN and ADN planning Types of cost/
decision/optimizing variables
PDN planning ADN planning
Installation/ reinforcement/
replacement cost for:
Substation, transformers, feeders, lines/
feeder branches, sectionalizing switches and tie-lines, conventional reactive power compensators, such as, voltage regulators, capacitors, and on-load tap changers etc.
DG units, storage units (i.e., battery), advanced protection and communication infrastructure, advanced automation and metering technologies, power electronics devices for reactive power compensation and PQ improvement etc.
Operational cost: Cost of energy loss and maintenance cost for each equipment associated with PDN
Cost of energy loss and maintenance cost for each equipment associated with ADN
Decision/optimizing variables:
Size and location for new substation, number of feeders, feeder routing, number and locations for sectionalizing switches and tie-lines, conductor types and sizes etc.
Sizes and locations for DG units, sizes and locations for battery, appropriate coordination of protection and automation technologies, generation mix among different types of DG units etc.
Substation
Tie line
Capacitor
1 2 3 4 5 6 7 8
9 10 11 12 13
14 15 16 17 18
(a)
(b) Charging
station
= Load = = Sectionalizing
switch DMS = Distribution management
system Substation
Tie line
Capacitor
1 2 3 4 5 6 7 8
9 10 11 12 13
14 15 16 17 18
DER
DER
DER DMS
Communication link
Fig. 1.1: The different features of typical distribution networks: (a) PDN and (b) ADN
Introduction and Literature Review
The difference in features between PDN and ADN is shown with example networks in Fig. 1.1. Usually, a typical PDN consists of single or multiple feeders, several sectionalizing switches, tie line(s), and capacitor bank(s) as shown in Fig. 1.1(a). In addition to these, a typical ADN, as shown in Fig. 1.1(b), consists of distributed energy resources (DERs), charging station for electric vehicles (EVs), smart meters (SMs), and distribution management system (DMS), which includes smart communication devices, advanced metering technologies, demand side management technologies, energy management technologies, smart automation technologies etc. Traditionally, the tie-line and sectionalizing switches in PDN are manually operated, whereas, advanced automation technologies are used to operate these in ADN. The scheduling for the operation of different DER units and charging station(s) is done using DMS. Hence, there are differences among the objectives and optimizing variables in PDN and ADN planning as summarized in Table 1.1. Thus, the ADN planning is an involved multi-objective optimization process with a number of objectives, such as, (i) minimization of installation/ reinforcement/ replacement cost for DG units and storage units (if any), (ii) minimization of operational cost (cost of energy loss and maintenance cost), (iii) maximization of reliability, (iv) maximization of DG capacity, (v) minimization of carbon emission, (vi) determination of optimal operational strategy for DG units, (vii) PQ mitigation etc. These objectives are optimized subjected to several technical and operational constraints, such as, active and reactive power balance, bus voltage magnitude, line current flow, PV capacity etc. In many occasions, these objectives do conflict with each other. For example, the integration of renewable DG unit reduces the carbon emission at the expense of higher investment cost. Hence, to solve the ADN planning problem consisting of several such conflicting objectives, one needs multi-objective optimization approach. There are various multi-objective optimization approach available in the literature. These are weighted aggregation-based approach, Pareto-based approach, ε-constrained approach etc. It is seen that the Pareto-based approach is popularly used in solving most of the multi-objective ADN planning problems.
The power systems researchers around the globe have significantly contributed on ADN planning during last 2-3 years. Hence, in this chapter, these works are systematically presented. A classification tree is developed for ADN planning, as shown in Fig. 1.2. It
consists of three levels based on different attributes of planning. The different types of ADN planning are grouped in Level #1 classification. The Level # 2 further categorizes the Level
#1 based on different load and generation models used. The Level # 3 classification is based on the different solution strategies reported in solving the ADN planning optimization problem. The special emphasis is given in the literature review to identify the inclusion of two important features of modern sustainable energy planning: (i) mitigation of the PQ problems and (ii) integration of storage units.
ADN planning
Conversion planning of PDN to ADN
ADN planning with smart grid features
Micro-grid planning
Investment planning
Operational planning
Investment and operational
planning
Investment planning
Operational planning
Investment planning
Operational planning
Investment and operational
planning
Constant Probabilistic Probabilistic Constant
Fuzzy
Constant Probabilistic
Fuzzy
Constant
Probabilistic Constant Constant
Probabilistic Constant
Probabilistic Probabilistic
Constant Level#1
Level#2
(A,B,A+V, Y,I)
(A,A+V, A+H,B,Y, I+J, D, I)
(A,Y,C,P,D ,L,I,H,Z1) Level#3
(A,G,C,M) (A)
(A,B,M,K) (A,B,Q)
(A)
(A) (C,Y,F) (R,F)
(D) (F)
(E,G,S,T+U, I,O,W)
(V+U,Z,X) (A,D, F+P)
(I,D,B, D,N)
A- Genetic algorithm (GA) and its variants
B- Particle swarm optimization (PSO) and its variants C- Non-linear programming (NLP)
D- Mixed integer programming (MIP) E- Dynamic programming (DP) F- Stochastic programming (SP)
G- Mixed integer non-linear programming (MINLP) H- Ant colony optimization (ACO)
I- Mixed integer linear programming (MILP) J- Simulated annealing (SA)
K- Chaotic local search with modified honey bee mating optimization (CLSMHBMO)
L- Multi period OPF (MOPF) M- Interior point method (IPM) N- Linear programing (LP)
O- Semi definite programming (SDP)
P- Monte Carlo method (MCM)
Q- Mixed integer second order cone programming (MISOCP)
R- Nash bargaining theory
S- Predictor corrector proximal multiplier (PCPM) T- Integer programming (IP)
U- Graph theory V- Tabu search (TS) W- Cultural algorithm (CA)
X- Self adaptive imperialist competitive algorithm (SAICA)
Y- Heuristic algorithm (HA)
Z- Active set optimization algorithm (ASOA) Z1- Multi-objective evolutionary algorithm (MOEA)
Fig. 1.2: Classification tree of ADN planning
Introduction and Literature Review