Index
Note: Page numbers followed by “f” and “t” refer to figures and tables, respectively.
A
Ant colony optimization (ACO) technique, 51–53
flowchart of, 52f
Anticipatory transmission planning, 179
Antigen population, 61
Artificial immune system (AIS) algorithm, 61–62
Artificial intelligence, 75–76
Artificial neural networks (ANNs), 160
-based models, 161t
ASHRAE (American Society of Heating, Refrigerating, and Air Conditioning engineers) model, 240
Autonomous energy system, 279
Average cost, 186
Average customer curtailment index (ACCI), 248
Average energy not supplied (AENS), 248
Average service availability/unavailability index (ASAI/ASUI), 247
B
Backward current sweep technique, 255
Balancing market, 117
Basic feasible solution, 37
Basis, defined, 35
aging model of a typical lead-acid battery, 286–288
performance model of a typical lead-acid battery, 285–286
Bayesian networks, 161t
Beta distribution, 243
Bidding strategy models for price-taker renewable energy owner, 125–127
basic mathematical formation, 125–126
examples, 126–127
“Big data” technologies, 173–174
Bioenergy, 13
Biomass models, 235–237
C
Capacity market, 118
Centralized versus decentralized decision-making process, 180–181
Charge controller, 288–289
Climate change, 3
Combined renewable power forecasting methods, 162–166
data postprocessing-based combined approaches, 165–166
data preprocessing-based combined approaches, 164–165
parameter-optimization-based combined approaches, 165
Competitiveness, 183
Computational statistics, 105
Concave function, 32
Constrained optimization, solution techniques for, 41–66
ant colony optimization (ACO), 51–53
artificial bee colony algorithm, 59–61
artificial immune system (AIS) algorithm, 61–62
firefly algorithm, 56–59
game theory optimization, 62–63
genetic algorithm (GA), 41–44
mixed integer linear programming (MILP) concept, simplex technique for, 63–64
optimal control technique, 64–66
particle swarm optimization (PSO) technique, 44–48
simulating annealing, 49–51
Tabu search, 53–56
Constrained optimization problem, 29–30
first stage constraints, 80–85
day-ahead market power balance, 85
generation side reserve scheduling, 81–82
generator minimum up and down time constraints, 80–81
generator output limits, 80
load serving entities, 83–85
ramp-up and ramp-down limits, 81
startup and shutdown costs, 81
unit commitment constraints, 81
wind-power scheduling, 83
linking constraints, 88–90
additional cost due to change of commitment status of units, 88
demand side reserve deployment, 89–90
generation side reserve deployment, 88–89
load following reserves determination, 90
second-stage constraints, 85–88
energy requirement constraint for LSE of type 1, 86
generating units, 85–86
involuntary-load shedding limits, 86
network constraints, 87–88
reserve deployment from LSE of type 2, 87
wind spillage limits, 86
Consumers, 118
Continuous variable optimization model, 33
Control variables, 29–30
Conventional energy market, 76
Conventional energy owner model, 129–131
Convex optimization, 31–32
Convex set, 32f
Cooptimization TEP, 179
Coordinate descent method, 41
Corrosion as aging factor, 286
Costumer-oriented reliability indices, 247
Cumulative distribution functions (CDFs), 170
Current trends and future prospects of integrating RESs, 22
Customer average interruption duration index (CAIDI), 247
Cut-in speed, 283–284
D
Data collection, 28–29
Day-ahead (DA) market, 117
uncertainties in, 142
Day-ahead market power balance, 85
Demand response (Dr), 75
Demand side reserve deployment, 89–90
Deterministic forecasts, 169–170
case study
case under study and assumptions, 267–271
simulation results, 271–273
classification, 234t
deterministic models, 234–237
biomass models, 235–237
geothermal models, 234–235
effects on distribution networks, 243–257
costs and benefits, 250–251
fault current, 253–254
harmonic distortion, 254–256
power distribution system reliability, 245–250
power losses, 244–245
reactive power supply, 256–257
voltage profile, 243–244
voltage stability, 251–253
stochastic models, 237–243
beta distribution, 243
extreme value distribution, 242
normal distribution, 242
PV unit models, 238–242
Weibull distribution, 242
wind unit models, 237–238
Dual of linear maximization problem in canonical form, 34–35
Dynamic optimization problems, 29–30
Dynamic programming, 75–76
problems, 27
analytical solution to the problem, 203–205
assumptions, 200–201
problem formulation, 201–203
E
Economic aspects of renewable energy systems, 15–18
affecting factors, 15–16
economic trend, 17–18
life-cycle costs, 16–17
ECOTOOL MATLAB toolbox, 90–91
Electric distribution network (EDN), 279
Electric distribution system reliability, DGs and, 248–249
Electric Reliability Council of Texas (ERCOT), 120–121
energy owners in, 119–120
importance of integrating renewables into, 120
participants and operators, 118
time frame of, 118
importance of integrating renewables, 120
participants and operators, 118
time frame of, 118
Electricity sector, sustainability in, 2f
Ellipsoid method, 35
Empirical mode decomposition (EMD), 164–165
Energy not supplied (ENS), 247
Error processing techniques, 166t
Exact loss, 244
Exploiting behavior, 45
Exploratory behavior, 45
Extreme value distribution, 242
F
Fault current, 253–254
calculation of, 254
contribution by upstream grid and DG interface, 253
contribution of DG unit, 254
contribution of upstream grid, 254
fault current limiters (FCLs), 253
Feasible set, 29
Fibonacci search, 39–40
Financial transmission rights (FTR) market, 117–118
Firefly algorithm technique, 56–59
flowchart of, 58f
First voltage stability index (FVSI), 251–252
Fuzzy logic models, 161t
G
Game, defined, 128
Game theory optimization, 62–63
Gaussian distribution principle, 56–57
Gaussian probability density function (PDF), 293
Generation side reserve deployment, 88–89
Generation side reserve scheduling, 81–82
flowchart of, 43f
Geothermal models, 234–235
global capacity and additions, 11t
Golden section search, 39
Gradient descent method, 40–41
Green energy market, 76
H
Harmonic distortion, 254–256
harmonic power flow analysis, 255
individual harmonic distortion limits, 256
total harmonic distortion limits, 255–256
total power loss, 256
Hooke and Jeeves’ method, 40
Hourly load profile, 291f
Hybrid energy systems modeling, 282–289
battery energy storage system modeling, 284–288
aging model of a typical lead-acid battery, 286–288
performance model of a typical lead-acid battery, 285–286
charge controller, 288–289
photovoltaic panel modeling, 282–283
power converter, 289
sizing and optimization, 290–291
wind turbine modeling, 283–284
Hybrid optimization model for electric renewables (HOMER), 281
Hybrid2 model, 281
Hydro power, 12–13
Hydro power global capacity and additions, 12t
Hydropower power technologies, 13t
Hyperplane, 35
I
Improved hybrid optimization by genetic algorithm (iHOGA), 281
Independent system operators (ISOs), 118
Individual harmonic distortion (IHD) limits, 256
Inelastic loads, 78
Infeasible linear programs, 34
Integer programming problem, 31
Integration of renewable energy system, 18–22
challenges and barriers, 19–21
challenges and barriers, alleviating, 21
current trends and future prospects, 22
opportunities, 18–19
K
Kalman filter models, 161t
L
Lagrangian relaxation, 75–76
Lead-acid battery
aging model of, 286–288
performance model of, 285–286
Least-squares problem, 30
Lerner index, 140
Life-cycle costs, 16–17
Line search algorithms, 38
Line stability factor (LSF), 252–253
Linear bidding function, 80f
Linear optimization problems, applied base technique to solve, 35–37
Linear transmission investment cost, 191
Linearized DG placement problem, 261–265
algorithm, 262–265
linear sensitivity, 261–262
Linking constraints, 88–90
additional cost due to change of commitment status of units, 88
demand side reserve deployment, 89–90
generation side reserve deployment, 88–89
load following reserves determination, 90
Load- and energy-oriented reliability indices, 247–248
Load and reserve scheduling from LSE of type 1, 83f
Load and reserve scheduling from LSE of type 2, 84f
Locational marginal prices (LMP), 118
Lyapunov stability criteria, 65
M
Market clearing model, 77f
Mathematical model, 77–90
constraints, 80–90
objective function, 79
overview and modeling assumptions, 77–78
Mathematical program with equilibrium constraints (MPEC) approach, 140–155
case study, 150–155
mathematical formulation and conversion, 144–150
bilevel problem, 144–147
model conversion, 147–149
reform MPEC as MILP, 149–150
multistage stochastic MPEC, 142–143
wind power energy owners as price-makers, 140–141
Matrix game, solving, 132–133
Mechanical energy, 13
Merchant interconnectors, 180–181
Meta-heuristic techniques, 75–76
Metropolis algorithm, 49
Metropolis criterion, 49
Mixed integer linear programming (MILP), 88–89
simplex technique for, 63–64
Model application and interpretation, 28–29
Model development, 28–29
Model validation and evaluation of the performance, 28–29
Multi-agent based market model, 76
Multiobjective programming problem, 31
Multistage stochastic programming approach, 122–140
bidding strategy models, 125–127
risk management, 124–125
scenario generation and reduction method, 122–123
trading risk, mitigating, 128–140
Mutation rate, 61
N
National Aeronautics and Space Administration (NASA), 291–292
Nelder–Mead method, 38–39
Neural network methods, 27
Nonlinear optimization algorithms, 38–39
Non-optimal control problems, 29–30
O
Objective function, 29, 29, 29–30, 30, 30, 30, 30–31, 31, 31, 34, 34, 37–38, 38, 38–39, 40, 40–41, 42, 49, 49, 54–55, 56–57, 60, 63, 79, 126, 144, 188, 191, 202–203, 205, 258, 267, 269–270
Opportunities of integrating RESs, 18–19
Optimal placement, of DG unit, 257–267
linearized DG placement problem, 261–265
algorithm, 262–265
linear sensitivity, 261–262
problem definition, 258–259
solution methodology, for sizing and siting of DGs, 259–261
analytical techniques, 259
artificial intelligence (Metaheuristic) techniques, 261
classical optimization techniques, 259–260
future use, 261
miscellaneous techniques, 261
stochastic assessment of DG placement problem, 265–267
handling stochastic analysis, 266
modeling uncertainties, 265–266
optimization method, 266
stochastic optimization algorithm, 266–267
Optimal procurement of contingency and load, 75
case studies, 90–105
application on a 24-bus system, 97–105
computational statistics, 105
illustrative example, 91–96
wind-power production scenario generation, 90–91
mathematical model, 77–90
constraints, 80–90
objective function, 79
overview and modeling assumptions, 77–78
Optimal solution of optimization problem, 29
Optimization problem, formulation of, 28–37
convex optimization, 31–32
general models of, 29–31
linear optimization, 32–37
applied base technique to solve, 35–37
duality, 34–35
Optimization tools, need for
to solve problems related to renewable energy systems, 23
Optimum bidding of renewable energy, 117
IEEE Reliability Test System, 157f
price-taker models
MPEC approach, See Mathematical program with equilibrium constraints (MPEC) approach
multistage stochastic programming approach, See Multistage stochastic programming approach
renewable energy system owners in electricity markets, 117–122
current market rules for, 120–122
electricity market overview, 117–120
importance of integrating renewables, 120
Optimum solution, 33
P
Paris Agreement, 3
Particle swarm optimization (PSO) technique, 44–48
flowchart of, 46f
Peak-load pricing, 186
Pennsylvanian–New-Jersey–Maryland interconnection (PJM), 120–121
Performance index (PI), 29–30
Photovoltaic irradiance and power forecasts, 161–162
Photovoltaic panel modeling, 282–283
Photovoltaic unit models, 238–242
Pivoting, 35
Plug-in-hybrid electrical vehicles, 60–61
Pool-based market, 117
Power converter, 289
Power distribution system reliability, 245–250
DGs and electric distribution system reliability, 248–249
reliability evaluation methods, 246
reliability indices, 246–248
reliability models of renewable generators, 249–250
reliability studies in distribution systems, 245–246
Power exchanges (PX), 183
Power inverter model, fitting of, 290f
DGs effects on distribution network, 244–245
total power loss, 256
regulated versus deregulated, 179
sources of operational flexibility in, 22f
wind–photovoltaic–battery hybrid power system, 47
wind–photovoltaic-fuel cell power system, 48
wind–solar power system, 48
wind–thermal hybrid power system, 61
Price-taker models
MPEC approach, 140–155
case study, 150–155
mathematical formulation and conversion, 144–150
multistage stochastic MPEC, 142–143
wind power energy owners as price-makers, 140–141
multistage stochastic programming approach, 122–140
bidding strategy models, 125–127
risk management, 124–125
scenario generation and reduction method, 122–123
trading risk, mitigating, 128–140
Primal-dual algorithms, 35
Problem definition and formulation, 28–29
Q
Quadratic programming problem, 30–31
R
Rated wind speed, 283–284
Reactive power supply, 256–257
incorporating reactive power to DG placement model, 257
use of power electronic systems, 257
VAR support, 256
uncertainties in, 142–143
Real-valued programming problem, 31
Regional transmission organizations (RTOs), 118
Reliability evaluation methods
analytical methods, 246
simulation methods, 246
Reliability indices, 246–248
Reliability models of renewable generators, 249–250
Reliability studies, in distribution systems, 245–246
Renewable electric power capacity, 5t
Renewable energy owner model, 131–132
Renewable energy trend, 3–4
Renewable generators, reliability models of, 249–250
Renewable power forecasting, 159
discussion, 173–174
fundamentals of, 160–162
PV irradiance and power forecasts, 161–162
wind speed and power forecasts, 160
in optimum power system operation, 170–173
state-of-the-art in, 162–170
combined renewable power forecasting methods, 162–166
probabilistic renewable power forecasting methods, 169–170
spatiotemporal renewable power forecasting methods, 167–169
Reserve scheduling
from generating units, 82f
methodology, 93–94
Retailers, 118
Risk management, impact of, 139–140
Rural electrification in a remote community, 291–300
Rural electrification rates per region, 280f
S
Schur formula, 65
Scout bees, 59–60
Security of energy supply, 183
Shepherd model, 286
Short-term forecasts, 170–171
Simplex algorithm, 37
for mixed integer linear programming concept, 63–64
flowchart of, 50f
Simulation and optimization parameters, 297t
Single-objective programming problem, 31
SiNGULAR project, 90–91
6-bus system, topology of, 92f
Small-capacity wind turbine, typical power curve of, 284f
Small-scale stand-alone hybrid renewable energy systems, optimum design of, 279
battery energy storage system modeling, 284–288
charge controller, 288–289
photovoltaic panel modeling, 282–283
power converter, 289
rural electrification in a remote community, 291–300
sizing and optimization, 290–291
wind turbine modeling, 283–284
concentrated solar power (CSP), 10
solar photovoltaic, 8–9
global capacity and additions, 9t
Solar power forecasts, 161–162
Solar–wind-battery hybrid system, 42–44
Space-time wind forecasts, 172
Spatiotemporal renewable power forecasting methods, 167–169
Spot-pricing theory, 186
Standard test conditions (STC), 282–283
State variables, 29–30
Static optimization problems, 29–30
Static transmission expansion planning (STEP), 192–200
analytical solution, 194–196
approximated HVDC power flow, 195–196
full HVDC power flow, 194–195
to the optimization problem, 197–199
assumptions, 192
Stochastic assessment of DG placement problem, 265–267
multistage stochastic programming approach, 122
SVM-based models, 161t
in the electricity sector, 2f
System average interruption duration index (SAIDI), 247
System average interruption frequency index (SAIFI), 247
T
Time series models, 161t
Total harmonic distortion (THD) limits, 255–256
Total power loss, as harmonic distortion, 256
Trading risk, mitigating, 128–140
Transmission expansion planning (TEP)
for AC networks, 189–190
algorithms and models for solving TEP, 205–206
exact algorithms, 206
heuristic and metaheuristic algorithms, 206
classification of TEP formulations, 178–183
centralized versus decentralized decision-making process, 180–181
deterministic versus nondeterministic methods, 182
regulated versus deregulated, 179
static versus dynamic methods, 182–183
continues social welfare, 190–191
dynamic TEP (DTEP), 200–205
analytical solution to the problem, 203–205
assumptions, 200–201
problem formulation, 201–203
in Europe, 183–185
for high voltage direct current (HVDC), 185
linear transmission investment cost, 191
numerical results, 211–217
numerical simulation, 207–221
assumptions, 207
data clustering, 209–210
defining development stages, 207–209
review of TEP formulations, 185–205
simplifying problem formulation, 189–191
static TEP (STEP), 192–200
approximated HVDC power flow, 195–196
assumptions, 192
full HVDC power flow, 194–195
Transmission system operator (TSO), 177
“20/20/20” targets, 183–184
24-bus system test system data, 106–109
Two-state model (ON–OFF model), 249–250
Types of renewable energy systems, 4–14
bioenergy, 13
geothermal power, 11
hydro power, 12–13
ocean power, 14
solar energy, 8–10
concentrated solar power (CSP), 10
solar photovoltaic, 8–9
wind power, 5–8
U
Unbounded linear programs, 34
coordinate descent method, 41
Fibonacci search, 39–40
golden section search, 39
gradient descent method, 40–41
Hooke and Jeeves’ method, 40
Nelder–Mead method, 38–39
Unrestricted variable, 33
V
Voltage profile
DGs effects on distribution network, 243–244
Voltage stability, 251–253
first voltage stability index (FVSI), 251–252
line stability factor (LSF), 252–253
W
Wavelet transform (WT), 164–165
Weather research and forecasting (WRF) model, 171–172
Weibull probability density function (PDF), 293–294
Weighted Ah throughput method, 285
Wind power global capacity and additions, 7t
Wind power total world capacity (2000–13), 7f
Wind turbine modeling, 283–284
Wind unit models, 237–238
Wind–photovoltaic powered hybrid system, 47
Wind–photovoltaic–battery hybrid power system, 47
Wind–photovoltaic-fuel cell power system, 48
Wind-power production scenario generation, 90–91
Wind-power scheduling, 83
Wind–solar power system, 48
Wind–solar–diesel generator–batteries–fuel cell-electrolyzer, 47
Wind–thermal coordination algorithm, 44
Wind–thermal hybrid power system, 61
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