Systematic Categorization of Optimization Strategies for Virtual Power Plants
Abstract
:1. Introduction
2. Framework and the Key Contributions
 To classify different optimization strategies for VPP based on system configuration, parameters, and control techniques.
 To summarize the methodology and objectives of every optimization scheme for VPP and to demonstrate the most feasible control scheme to maximize the profits with minimum cost.
 To depict the procedure of control flow of VPP that consists of DERs and ESSs.
 To perceive the market utilities and consumers demand responses and to designate the necessary model to deal with the electricity market.
 To analyze the causes for uncertainties in the electricity market and to demonstrate the necessary techniques to eliminate the uncertainties.
3. Methodology
4. Virtual Power Plants
4.1. Commercial Virtual Power Plant
4.2. Technical Virtual Power Plant
4.3. Objective Functions and Constraints
5. Virtual Power Plant Optimization Models, Techniques, and Algorithms
5.1. Conventional VPP Optimization Strategy
5.2. Offering Model
5.3. Optimization Algorithm Based on Intelligent Techniques
 (i)
 Load distribution by generation units with priority level and
 (ii)
 Load distribution by generation units equally. Another, MAS for VPP is presented in [93] to control the generation units’ carbon emissions.
5.4. PricedBased Unit Commitment (PBUC) Model
5.5. Optimal Bidding Strategy
5.6. Optimization Algorithm Based on Linear Programming
5.7. Optimization Algorithm Based on Stochastic Technique
6. Conclusions and Future Directions
 The offering strategy that utilizes wind power and demand responses overcomes the intermittence problem in the electricity market. The system also balances the electricity market prices with an improving profit. A fastcomputation can be achieved if the offering model is combined with stochastic programming.
 Like fuzzy logic, intelligent control can maintain priority management among suppliers, consumers, and demand responses. Furthermore, a flexible smart grid can be established using MAS that includes ANN, levelbased approach, and EVPP.
 PBUC method utilizes DERs in DA market scheduling to handle uncertainties in the electricity market.
 Bidding strategies (BSs) can maximize profits. The BSs with twostage robust optimization can eliminate uncertainties along with maximizing profits. The security can also be ensured by BDT based BS.
 DLC, based on linear programming, successfully handles both the transmission and distribution system. CCS can govern power in a broader range. Linear programmingbased control maximizes profits as well as lessens risk.
 Bilevel or multilevel stochastic optimization can deal with uncertainty parameters such as wind power generation, DA market scheduling, and ultimately provides maximum profits.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
ABC  AgentBased Control 
ABP  AgentBased Approach 
ALDFS  Adaptive Load Dispatching and Forecasting Strategy 
ANN  Artificial Neural Network 
BDT  Bender Decomposition Technique 
BLMOA  BiLevel MultiObjective Approach 
BLSSOM  BiLevel Stochastic Scheduling Optimization Mode 
BM  Business Model 
CCC  Control Coordination Center 
CCS  Coordinated Control Strategy 
CGT  Conventional Gas Turbine 
CHP  Combined Heat and Power 
CIDOA  Combined Interval and Deterministic Optimization 
CPP  Conventional Power Plant 
COA  Combined Optimization Algorithm 
CVaR  Conditional Value at Risk 
DA  DayAhead 
DER  Distributed Energy Resources 
DG  Distributed Generation 
DHDCA  Direct, Hierarchical and Distributed Control Approach 
DLCM  Direct Load Control Model 
DSM  Demand Side Management 
DSO  Distribution System Operator 
EEX  European Energy Exchange 
EMM  Energy Management Model 
EMS  Energy Management System 
EPEX  European Power Exchange 
ESS  Energy Storage System 
EVMM  Electric Vehicle Management Module 
FC  Fuel Cell 
FLC  Fuzzy Logic Control 
FMOOA  Fuzzy Multiple Objective Optimization Algorithm 
FRS  Frequency Regulation Strategy 
FSD  FirstOrder Stochastic Dominance 
GDCS  Generation Driver Control Strategy 
IAS  Intelligent Autocontrol System 
IM  Instant Messaging 
IOE  Internet of Energy 
IVPP  Industrial Virtual Power Plant 
KKT  KarushKuhnTucker 
LWC  Levelized Fresh Water Cost 
MAS  MultiAgent System 
MBM  MarketBased Model 
MCOS  MultiCriteria Operation Strategy 
MILP  Mixed Integer Linear Programming 
MM  Mathematical Model 
MORSM  MultiObjective Robust Stochastic Model 
MOSS  MultiObjective Stochastic Scheduling 
MRTS  Mixed Rental Treading Strategy 
NDRM  Novel Demand Response Model 
NLMT  NonLinear Minimization Technique 
NLMIPM  NonLinear Mixed Integer Programming Model 
NOS  Novel Optimal Scheduling 
NSPA  Novel Stochastic Programming Approach 
OA  Optimization Algorithm 
OBS  Optimal Bidding Strategy 
ODS  Optimal Dispatch Scheduling 
OOM  Offering Optimization Model 
OS  Offering Strategy 
PBUC  PriceBased Unit Commitment 
PEM  Point Estimate Model 
PM  Probabilistic Method 
PSO  Particle Swarm Optimization 
PV  Photovoltaic 
RAM  Risk Aversion Model 
RES  Renewable Energy Resources 
RHS  Risk Hedging Strategy 
RT  RealTime 
RTS  Reliability Test System 
SAEV  Standard Autonomous Electric Vehicle 
SAROS  Stochastic Adaptive Robust Optimization Scheme 
SCADA  Supervisory Control and Data Acquisition 
SDM  Stochastic and Deterministic Model 
SOA  ServiceOriented Architecture 
SPBSA  Stochastic ProfitBased Scheduling Approach 
TPA  Trip Prediction Algorithm 
TRM  Two Risk Management 
TRM  Two Risk Management 
TSO  Transmission System Operator 
TSOSM  TwoStage Optimal Scheduling Model 
TSSP  TwoStage Stochastic Programming 
TVPP  Technical Virtual Power Plant 
VOIP  Voice Over IP 
Nomenclature
$f$  The total fuel cost for all the energy resources 
${C}_{Grid}$  The cost of electric energy in terms of grid 
${\eta}_{H}$  Heat generator 
${\eta}_{P}$  Power generator 
${\eta}_{Hc}$  Combined heat and power plant 
${T}_{tot\_cost}$  The total cost 
$A{V}_{el}$  The weighted average of the electricity cost 
β  The deterministic factor 
ξ  The degree of pessimism of the distribution market 
${y}_{0}$  The random forecasted values 
${P}^{d}$  Deterministic profits 
${L}_{t}^{VPP}$  The total hourly load of VPP 
${\lambda}_{t}^{w}$  The hourly DA wholesale market prices 
${U}^{}$  The total system disutility 
${\rho}_{EM,t}^{f}$, ${\rho}_{EM,t}^{\omega}$  The forecasted and scenariodependent prices in the electricity market, respectively 
${P}_{sel,zt}^{\omega}$  The scenariobased electricity demand 
${P}_{sel,zt}^{s}$  The scheduled served electricity demand in period t and zone z 
${\pi}_{\omega ,t}$  The probability of occurrence of scenario ω in period t 
${\rho}_{SR,t},{\rho}_{re{t}_{vpp},t}$, ${\rho}_{ens,t}$  The spinning reserve market price, VPP’s retail energy rate, and considered penalty of nonreserved in period t respectively. 
${n}_{0}$  The number of device off 
${n}_{c}$  The number of devices operating under curtailed power 
${n}_{n}$  The number of devices operating under normal rated power 
${C}_{u,t}^{EP}$  The price of earned energy from EP u during period t 
${C}_{t}^{gas}$  The price of gasoline during time t 
${e}_{u,t}^{EP}$  The energy supply by EP u during period t 
$C{S}_{v}$  The average gasoline usage of PHEV 
${\delta}_{v,t}$  The depth of discharge (DOD) for PHEV v during period t 
$f\left(\delta \right)$  The probable battery replacement cost as a function of DOD 
${d}_{v,t}^{CS}$  The chargesustaining (CS) mode during period t by PHEV v 
$c\left(t\right)$  The charge energy 
${C}_{bat}$  The cost of the battery 
${L}_{bat}$  The life of the battery in equivalent full cycle 
${C}_{DG}\left(t\right)$  The cost function of DG 
${C}_{grid}$  The cost function of the grid 
${\gamma}_{cycles}$  The battery cost during opposite aging 
${\pi}_{r}^{down}$, ${\pi}_{r}^{up}$  The probability of the ${r}^{\mathrm{th}}$ downregulation and upregulation price scenario, respectively 
${\psi}_{r}^{down}$, ${\psi}_{r}^{up}$  The downregulation and upregulation price ratio in time t respectively 
$b{m}_{wp}^{down}$, $b{m}_{wp}^{up}$  The power sell and purchase in balancing market, respectively 
$SU{C}^{c}$  The CPP startup cost 
${C}_{wp}^{c}\left(t\right)$  The total cost of the CPP electricity production 
${\lambda}_{p}\left(t\right)$  The DA market price in time t and scenario p 
${E}_{d}$  Annual electricity that is not supplied or brought from the grid 
${V}_{oll}$  The value of the lost load 
${H}_{prod}$  The annual hydrogen production 
${W}_{prod}$  The annual freshwater production 
${E}_{b}$  The annual electricity brought from the grid 
${T}_{tot\_el}$  The total generated electricity 
${E}_{pr}$  The electricity price of the grid 
${P}_{DL}^{k}$  The curtailment value of dispatchable load in an hour 
${C}_{DL}^{k}$  The cost of an interruptible consumer to curtail its load in an hour 
${\alpha}_{DG}^{k}$, ${\beta}_{DG}^{k}$, ${\gamma}_{DG}^{k}$  The binary decision (on = 1, off = 0) for DG unit status, startup and shunt down in hours, respectively 
${C}_{DG}$, ${C}_{SG}$  The generation costs of dispatchable and stochastic DG unit, respectively 
$SU{C}_{DG}$, $SD{C}_{DG}$  The startup and shut down the cost of DG unit, respectively 
$LOA{D}_{t}$  The total forecasted load of VPP 
$S{C}_{dg,i,t}$, $SH{C}_{dg,i,t}$  The shut up and shut down the cost of DG 
${\rho}_{E,t}$  The price of the energy market 
${\rho}_{R,t}$  The price of spinning reserve market 
${\rho}_{L,t}$  The retail energy rate of VPP 
${P}_{curt,i,t}$  The unserved load for trading in the energy market 
${P}_{dg,i,t}$  The generation of DG in the energy market 
${G}_{P}\left(t\right)$  The dayahead market 
${P}_{wsp}^{up}$  The electricity sells 
${P}_{wsp}^{down}$  The electricity purchase 
B  The importance of risk minimization 
${C}_{conv}\left(t\right)$  The function of the cost of electricity production from CPP 
${y}_{conv}\left(t\right)$  1 when CPP is started from the beginning period t and is 0 at other cases 
${S}_{conv}$  The startup cost of CHP 
${G}_{th}$  The earnings from selling thermal energy 
${G}_{EEX}$  The earnings from selling electrical energy 
${C}_{chp}$  The fuel and operational cost of CHP 
${C}_{boiler}$  The fuel and operational cost of the boiler 
α  The setting confident level 
${\zeta}_{\omega}$  The auxiliary variable to compute CaVR 
${\pi}_{\omega}$  The probability of occurrence of scenario ω 
${\Pi}_{t\omega}$  The profit realized in time t and scenario ω 
${P}_{SR,ts}$  The exchange power between VPP and spinning reserve market at hour t and scenario s 
${P}_{sel,zts}$  The served electric load power ai time t, zone z, and scenario s 
${P}_{drp,zts}^{I}$, ${P}_{drp,zts}^{II}$, ${P}_{drp,zts}^{III}$  The first, second, and thirdlevel electric load curtailment respectively of DR at hour t, zone z, and scenario s 
${P}_{ens,zts}$  The amount of unserved energy in zone z, hour t, and scenario s 
${P}_{line,zt}$  The cross power of upstream line ai zone z and hour t 
$H{V}_{NG}$  The heating value of natural gas 
${\eta}_{boil,z}$, ${\eta}_{chp,z}$  The boiler efficiency and CHP electrical efficiency in zone z, respectively 
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Ref  Optimization Approach  System Configuration  Key Features 

[40]  Energy management model  DER and ESS 

[47]  A probabilistic method  Electrical and thermal energy resources 

[60]  A novel demand response model  DER 

[67]  Combined interval and deterministic optimization approach  Small scaled DER 

[68]  Nonlinear minimization techniques  CHP plant, electrical and thermal energy storage system, energy exchanger 

[69]  Optimization algorithm  CHP, Renewable energy sources (PV, wind) 

[70]  Nonlinear programming based optimization algorithm  Wind power generation unit 

[71]  A direct, hierarchical, and distributed control approach  DER 

[72]  A marketbased VPP model  DER 

[74]  Optimal operation of VPP  DER 

[75]  An adaptive load dispatching and forecasting strategy  Solar, wind, hydrogen, and thermal power system 

[76]  Generation driver control strategy  Solar, wind, and energy storage system 

[77]  VPP optimization scheme  DER and ESS 

[78]  An optimization model for SAEV and VPP  Solar, wind, and dispatch generation units 

[79]  Tripprediction algorithm  Renewable energy resources (RERs) and ESS 

[80]  The multiobjective robust scheduling model  WPP, PV, gas storage tank 

[81]  Mixed rentaltrading strategy  DER, Battery, and EV 

[82]  Frequency regulation strategy  DER, flexible loads, and ESS 

[83]  The twostage optimal scheduling model  Energy storage system (Battery), Gasfired microturbine, wind power generators 

Ref  Optimization Approach  System Configuration  Key Features 

[86]  Offering a model based on twostages stochastic programming  DERs (wind and cascade hydropower system) 

[87]  Offering a model based on a twostage stochastic mixedinteger linear programming scheme  DERs, storage facility, and a power plant (wind power plant) 

[88]  Offering optimization model  WPP and storage unit (flexible loads) 

[89]  Risk aversion model  WPP 

Ref  Optimization Approach  System Configuration  Key Features 

[46]  A multiagent system (MAS)  PV, wind, biomass plant, EV, and ESS 

[90]  Fuzzy multiple objective optimization algorithms  Renewable generation sources 

[91]  Agentbased control  PV generator, wind turbine, battery, diesel generator, and CHP plant 

[92]  Intelligent Autocontrol System (IAS)  Distributed energy resources (DER) and flexible loads 

[93]  A multiagent system (MAS)  PV, battery, fuel cell, diesel generator, and micro CHP plant 

Ref  Optimization Approach  System Configuration  Key Features 

[2]  A probabilistic PBUC approach for optimal bidding of VPP  DER, ESS sand loads 

[41]  PBUC model  Energy Storage, flexible loads, and CPP 

[94]  PBUC model  PV, wind power plant, flexible loads, energy storage, and CPP 

Ref  Optimization Approach  System Configuration  Key Features 

[95]  Optimal bidding strategy for commercial virtual power plant (VPP)  Distributed energy resources (DER), battery storage systems (BSS) 

[96]  Optimal bidding strategy based on mixedinteger linear programming algorithm  Solar, wind, and gas power plant 

[63]  Bidding strategy for the nonlinear mixedinteger programming model  DER 

[97]  Optimal bidding strategy  Combined heat and power (CHP) and renewable energy resources (RES) 

[98]  Bidding strategy  Wind power plant (WPP) 

[100]  A mathematical model based on Benders decomposition technique  DER and VPP security constraints 

[101]  Bilevel multiobjective approach  DER 

Ref  Optimization Approach  System Configuration  Key Features 

[37]  Mixedinteger linear programming (MILP) based optimization algorithm  DER, ESS, and CHP plant 

[44]  An optimization algorithm  CHP system 

[57]  Combined optimization algorithm  Micro CHP system and PV power plant 

[59]  An agentbased approach for VPP based on linear programming  Wind power generator and electric storage system (battery) 

[102]  Mixedinteger linear programming (MILP) optimization method  RER, ESS, and CPP 

[103]  Twostage stochastic mixedinteger linear programming based optimization algorithm  DER (PV, wind firms, microturbine, diesel generator, and battery banks) 

[104]  Two risk management approach based interactive cooperation model  DER 

[105]  Coordinated control strategy  PV and controllable loads 

[106]  A risk hedging strategy for commercial VPP  DER 

[107]  Multicriteria operation strategy  Wind, solar, CHP, EV, and other electric storage systems 

[108]  A novel optimal scheduling model based on twostage stochastic mixedinteger linear programming  DER and ESS 

[109]  Direct load control model based on linear programming  DER 

[110]  Demandside management (DSM) program  DER, ESS 

[111]  Optimal dispatch scheduling  RES and CPP 

[112]  A VPP model  Wind, solar, fuel cell, and battery storage 

Ref  Optimization Approach  System Configuration  Key Features 

[39]  Stochastic adaptive robust optimization scheme for offering strategy  DER, ESS 

[48]  The stochastic profitbased scheduling approach  DER 

[66]  A multiobjective stochastic scheduling optimization model  PV power plant, wind power plant, CPP, and EV 

[113]  Twostage stochastic programming based optimization algorithm  Wind energy and hydropower storage 

[114]  A novel stochastic programming approach  DER 

[115]  A bilevel stochastic scheduling optimization model  PV, wind, conventional gas turbine (CGT), and ESS 

[116]  A stochastic and deterministic model  RES 

[117]  A probabilistic model  Thermal and electrical resources 

Ref.  Optimization Scheme  Offering Model  Intelligent Technique  PBUC  Bidding Strategy  Linear Programming  Stochastic Technique 

[2]  Probabilistic PBUC approach  ✕  ✕  ✓  ✕  ✓  ✕ 
[37]  MILP based optimization algorithm  ✕  ✕  ✕  ✕  ✓  ✕ 
[39]  The stochastic adaptive robust optimization scheme  ✓  ✕  ✕  ✕  ✕  ✓ 
[41]  PBUC model  ✕  ✕  ✓  ✕  ✕  ✕ 
[46]  A multiagent system (MAS)  ✕  ✓  ✕  ✕  ✕  ✕ 
[48]  The stochastic profitbased scheduling approach  ✕  ✕  ✕  ✕  ✕  ✓ 
[57]  Combined optimization algorithm  ✕  ✕  ✕  ✕  ✓  ✕ 
[59]  Agentbased approach  ✕  ✓  ✕  ✕  ✓  ✕ 
[63]  Optimal bidding strategy  ✕  ✕  ✓  ✓  ✕  ✕ 
[66]  Multiobjective stochastic scheduling  ✕  ✕  ✕  ✕  ✕  ✓ 
[86]  Offering model based on twostage stochastic programming  ✓  ✓  ✕  ✕  ✕  ✓ 
[87]  Offering model based on twostage stochastic MILP scheme  ✓  ✕  ✕  ✕  ✓  ✓ 
[88]  Offering optimization model  ✓  ✕  ✕  ✕  ✕  ✕ 
[89]  Risk aversion model  ✓  ✕  ✕  ✕  ✕  ✕ 
[90]  Fuzzy multiple objective optimization algorithms  ✕  ✓  ✕  ✕  ✕  ✕ 
[91]  Agentbased control  ✕  ✓  ✕  ✕  ✕  ✕ 
[92]  Intelligent Autocontrol System  ✕  ✓  ✕  ✕  ✕  ✕ 
[93]  A multiagent system (MAS)  ✕  ✓  ✕  ✕  ✕  ✕ 
[95]  Optimal bidding strategy  ✕  ✕  ✕  ✓  ✓  ✕ 
[96]  Optimal bidding strategy  ✕  ✕  ✕  ✓  ✓  ✕ 
[97]  Optimal bidding strategy  ✕  ✕  ✕  ✓  ✓  ✕ 
[100]  Mathematical modeling with Benders decomposition technique  ✕  ✕  ✕  ✓  ✕  ✕ 
[101]  Bilevel multiobjective approach  ✕  ✕  ✕  ✓  ✓  ✕ 
[109]  Direct load control  ✕  ✕  ✕  ✕  ✓  ✕ 
[102]  MILP optimization method  ✕  ✕  ✕  ✕  ✓  ✕ 
[103]  Twostage stochastic MILP based optimization algorithm  ✕  ✕  ✕  ✕  ✓  ✓ 
[104]  Interactive cooperation model  ✕  ✕  ✕  ✕  ✓  ✓ 
[105]  Coordinated control strategy  ✕  ✕  ✕  ✕  ✓  ✕ 
[106]  Risk hedging strategy  ✕  ✕  ✕  ✕  ✓  ✓ 
[107]  Multicriteria operation strategy  ✕  ✕  ✕  ✕  ✓  ✕ 
[108]  A novel optimal scheduling model  ✕  ✕  ✕  ✕  ✓  ✓ 
[110]  Demandside management (DSM)  ✕  ✕  ✕  ✕  ✓  ✕ 
[111]  Optimal dispatch scheduling  ✕  ✕  ✕  ✕  ✓  ✕ 
[113]  Twostage stochastic programming  ✕  ✕  ✕  ✕  ✕  ✓ 
[114]  Novel stochastic programming approach  ✕  ✕  ✕  ✕  ✕  ✓ 
[115]  Bilevel stochastic scheduling  ✕  ✕  ✕  ✕  ✕  ✓ 
[116]  The stochastic and deterministic model  ✕  ✕  ✕  ✕  ✕  ✓ 
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Podder, A.K.; Islam, S.; Kumar, N.M.; Chand, A.A.; Rao, P.N.; Prasad, K.A.; Logeswaran, T.; Mamun, K.A. Systematic Categorization of Optimization Strategies for Virtual Power Plants. Energies 2020, 13, 6251. https://doi.org/10.3390/en13236251
Podder AK, Islam S, Kumar NM, Chand AA, Rao PN, Prasad KA, Logeswaran T, Mamun KA. Systematic Categorization of Optimization Strategies for Virtual Power Plants. Energies. 2020; 13(23):6251. https://doi.org/10.3390/en13236251
Chicago/Turabian StylePodder, Amit Kumer, Sayemul Islam, Nallapaneni Manoj Kumar, Aneesh A. Chand, Pulivarthi Nageswara Rao, Kushal A. Prasad, T. Logeswaran, and Kabir A. Mamun. 2020. "Systematic Categorization of Optimization Strategies for Virtual Power Plants" Energies 13, no. 23: 6251. https://doi.org/10.3390/en13236251