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This paper presents a profit-based self-scheduling framework for generation company participation in energy and ancillary service markets under multi-constrained environment with renewable energy participation. The participation strategies of generation company include various objectives incorporating economic (profit maximization), environmental (emission minimization), and social (maximum load satisfaction) aspects. The total objective under single, dual and multi-constrained approaches is formulated along with appropriate constraints for energy, spinning reserve, and non-spinning reserve offerings in various markets. In addition, the impact of renewable energy participation in energy market on scheduling decisions of generation company in different markets is also examined. The renewable energy independent power producers namely wind energy and solar photovoltaic energy generators are considered in this study. The sensitivity analysis is also carried out to examine the impact of reserve deployment probability on optimal offerings and generation company surplus in energy as well as ancillary service markets. To solve the proposed framework, binary fireworks algorithm is used, considering the binary natured commitment problem of generation company’s thermal units. The simulation results of proposed framework tested using thermal units, wind energy independent power producer, solar photovoltaic independent power producer are presented for base case and various scenarios involving single, dual and multi objectives. The comparison shows the effectiveness of proposed multi-constrained approach in arriving at optimal offering of generation company under economic, environmental and social constraints. Therefore, this integrated approach can prove to be an effective tool for generation company participation in energy and ancillary service market under renewable participation.

This paper proposes a new methodology for scheduling of thermal generation plants equipped with Amine based post combustion carbon capture technology. The methodology develops a generic and simplified model for operational planning of thermal generators through unit commitment. The performance indices developed in this paper can accommodate the resource sensitivity of the fuel used for combustion. The proposed performance indices are coupled to unit commitment procedure through intelligent scheduling methodology considering both economic and environmental concerns of the system. The proposed models can be readily integrated into the unit commitment algorithm using the test system data and fuel composition. The same provides a simple and near appropriate model to consider resource sensitivity as compared to detailed (or) dedicated modeling carried out in Aspen, APEA, promax etc. The impact of resource sensitivity and scheduling strategy has been illustrated using a 10 unit thermal generation test system with amine based post combustion carbon capture technology. The resource sensitivity of the model has been demonstrated using three different compositions of fuel/coal viz. sub-bituminous, bituminous and lignite coals. The simulation results of resource sensitivity at various capture efficiencies are presented and discussed in detail with respect to various performance indices developed. The models developed in this paper can be integrated to wide range of thermal generation plants with wide range of coal type (or) fuel compositions across the globe.

This paper presents a dynamic energy balancing cost model for day ahead market scheduling under generation uncertainties and demand response penetration. The proposed dynamic models include temporally coupled system conditions to model various types of costs incurred due to uncertain wind energy generation and conventional generation uncertainty. The cost models of wind energy schedule in real-time markets for the deviation from DA estimations is modeled using compensation cost, dynamic adjustment cost, and rescheduling cost. The generation uncertainties and outages considered for conventional generation facility is expressed using geometric distribution to include temporal characteristics of generation outage rate. The problem is formulated as an operational cost minimization problem. The efficacy of the proposed dynamic balancing cost approach is verified using various levels of uncertainty. Further, sensitivity analysis is carried out with respect to various levels of wind penetration and outage rates for all the technoeconomic aspects of resource scheduling. The simulation results for the proposed approach are presented and discussed against static penalty cost methodology. The sensitivity analysis demonstrate the superiority of the proposed dynamic cost models in terms of technoeconomic performance attributes compared to the static cost models.

This paper proposes a quantum inspired binary grey wolf optimizer (QI-BGWO) to solve unit commitment (UC) problem. The QI-BGWO integrates quantum computing concepts with BGWO to improve the hunting process of the wolf pack. The inherent properties of Q-bit and Q-gate concepts in quantum computing help in achieving better balance between exploration and exploitation properties of the search process. The position update processes of wolves at different hierarchy levels are replaced by Q-bit's individual probabilistic representation along with dynamic rotation angle and coordinate rotation gate. Therefore, solution approaches exploit the search properties of GWO and quantum computing using quantum bits, gates, superposition principle etc., to solve the unit commitment schedule efficiently. The results and statistical analysis demonstrates the effectiveness of proposed approaches in solving the UC problem and establishes the significance of proposed approaches among existing binary and quantum computing based heuristic approaches.

This paper presents a computationally intelligent hybrid approach to incorporate the temporal characteristics of customer baseline load (CBL) in demand response exchange (DRX) mechanism using adaptive fuzzy inference system (FIS). The proposed hybrid approach considers the temporal characteristics of load profile using utilization factor, availability factor alongside the conventional/traditional willingness factor. The relation between load criticality and flexibility in terms of utilization and availability factors has been established and incorporated into the DR seller/customer bids in DRX through dynamic costing. Various models viz. linear, nonlinear, and exponential model etc., are developed to assess varying behavior of customer with respect to the CBL profile. In addition, a FIS is developed in this paper to account for uncertain/indistinct nature of input/information provided by the customer. To improve the performance of DRX market clearing, parameters of membership functions used in FIS are adaptively tuned using heuristic approaches. The performance of proposed hybrid model using FIS is compared with the traditional approach, fuzzy, and nonfuzzy approach without considering temporal characteristics, fuzzy, and nonfuzzy approaches with temporal characteristics only. The simulation results are presented and they demonstrate the superiority of FIS based hybrid model with CBL temporal characteristics through dynamic costing when compared to other models.

This paper investigates the impact of load profiling attributes on the demand response exchange (DRX) mechanism. Modeling of demand response (DR) seller bids in traditional approach/model includes a customer willingness factor assigned in a random/arbitrary manner. The proposed model of DR bids takes into account, the load attributes such as utilization and availability factors. The criticality and willingness of the DR load/customer are embedded into seller market bids through utilization and availability factor, respectively. Various cost models have been developed to emulate the possible customer behavior in DR cost modeling. However, modeling of indistinct and uncertain nature of customer behavior is a complex issue in real-time consideration. Therefore, this paper also presents a fuzzy inference system (FIS) for considering the customer load profile attributes in DR bids. In addition, parameter tuning of fuzzy membership functions is also carried out in this paper using heuristic optimization techniques to improve the performance of FIS in DRX clearing. The proposed methodology with load profile attributes is simulated considering various load types across different load sectors. Simulation results of proposed tuned FIS-based DRX clearing are compared with other nonfuzzy models, conventional models without load attributes and untuned FIS system to demonstrate the effectiveness of tuned FIS with load profile attribute consideration in DRX clearing.

The unit commitment problem belongs to the class of complex large scale, hard bound and constrained optimization problem involving operational planning of power system generation assets. This paper presents a heuristic binary approach to solve unit commitment problem (UC). The proposed approach applies Binary Grey Wolf Optimizer (BGWO) to determine the commitment schedule of UC problem. The grey wolf optimizer belongs to the class of bio-inspired heuristic optimization approaches and mimics the hierarchical and hunting principles of grey wolves. The binarization of GWO is owing to the UC problem characteristic binary/discrete search space. The binary string representation of BGWO is analogous to the commitment and de-committed status of thermal units constrained by minimum up/down times. Two models of Binary Grey Wolf Optimizer are presented to solve UC problem. The first approach includes upfront binarization of wolf update process towards the global best solution (s) followed by crossover operation. While, the second approach estimates continuous valued update of wolves towards global best solution(s) followed by sigmoid transformation. The Lambda-Iteration method to solve the convex economic load dispatch (ELD) problem. The constraint handling is carried out using the heuristic adjustment procedure. The BGWO models are experimented extensively using various well known illustrations from literature. In addition, the numerical experiments are also carried out for different circumstances of power system operation. The solution quality of BGWO are compared to existing classical as well as heuristic approaches to solve UC problem. The simulation results demonstrate the superior performance of BGWO in solving UC problem for small, medium and large scale systems successfully compared to other well established heuristic and binary approaches.

In the rapidly evolving modern grid technologies and deregulated electricity markets, sustainability needs are given a prime importance besides achieving energy sufficiency. To investigate the same, a multi-objective function, including both economic as well as environmental interests is formulated. Pertaining to the inclusion of renewable energy technologies (RETs), constraints and penetration bounds are introduced. In addition, to examine the tradeoff criteria in preservation of environmental and economic interests, pareto sets are obtained and also, a simple optimality condition for selection of near optimal solution from the obtained pareto set is proposed. Further, various scheduling alternatives/scenarios and cases are proposed to analyze the scheduling scenarios against operation strategies of different markets. Simulation results are presented and discussed in detail. Observations of the pareto sets revealed that, RET schedules in pareto dominant solutions are affected by scheduling scenario. The intervention of RET affected the optimal tradeoff between economic and environmental interests. Therefore, optimal scheduling pertaining to particular scenario affects the energy schedule of both conventional, RET and thereby affecting resultant profit and emissions.

This study presents two-level static and dynamic responsive load scheduling strategies for optimal tradeoff between scheduling preferences of demand response provider (DRP), demand response provider agent (DRPA) and distribution system operator (DSO) using moving time horizon control for real-time scheduling. The first level comprises of DRP scheduling with the objective of minimizing the electricity bill payments. The second stage includes a rescheduling strategy aided by DSO scheduling preferences constrained by the economic preferences of consumers set up in the first level of the scheduling strategy. There are two strategies for the second level: namely, static residential demand response (S-RDR), and dynamic residential demand response (D-RDR), which differ by the time sequence of operation and interactions between DRPA and DRPs. In S-RDR, the interactions between the two are limited to single event-acknowledge policy wherein all the DRPs act to schedule their responsive loads in a day ahead fashion. However, in D-RDR, multiple/continuous iterations take place between DRPA and DRPs with the consideration of optimal performance with respect to real-time market prices. The proposed framework is simulated using IEEE 34 bus distribution system. The simulation results are presented and discussed to analyse and compare the performance of proposed scheduling strategies with tariff-based and centralized scheduling approaches.

The profit-based unit commitment (PBUC) procedure is a binary natured operational planning problem of generation company (GENCO) in competitive electricity market. The GENCO solves PBUC problem in day ahead market of energy and reserve markets by commitment and scheduling of thermal units with an objective of profit maximization for the set of given price and load forecasts. Therefore, the solution quality and efficacy of PBUC optimization problem play a vital role in ensuring maximum returns to the GENCO. This paper presents a mathematical model-inspired heuristic approach called binary sine–cosine algorithm (BSCA) for solving PBUC problem. The SCA algorithm is a heuristic approach that uses the fluctuating nature of individual candidates/search agents in the search space around the global solution. The fluctuating mechanism is realized by using mathematical functions, i.e. sine and cosine functions. The proposed binary approach of SCA algorithm uses modified sigmoidal transformation function for binary mapping of continuous real-valued search space to binary counterpart. The efficacy of proposed approach in terms of solution quality and convergence is demonstrated using test system with different market participation policies. The results demonstrate the effectiveness of proposed BSCA approach over existing approaches for solving PBUC.

Concerns over rapid transformation of global climate patterns resulted in wide deployment of renewable energy sources especially wind energy. However, intermittent nature of wind energy makes resource scheduling aspect of system operator more complex. The emerging deregulation policies have added to the complexity of scheduling problem. Therefore, treating and scheduling of wind energy on deterministic basis would result in non-optimal energy dispatch and increased operation cost over scheduling horizon. This paper focuses on optimal scheduling methodology of wind energy in day ahead market (DAM) considering expected costs due to wind uncertainty in real time (RT) spot market under deregulated market structure. This paper introduces various possible costs, namely, spot market compensation cost, additional reserve cost and rescheduling cost applicable to deviation in scheduled power from DAM to spot market. The costs can be derived from front-end information of DAM, historical and statistical estim...

The electric sector contributes substantially to both greenhouse gas (GHG) and non-greenhouse gas (NGHG) emissions, which means that both conventional and thermal generation companies (GENCOs) must follow certain environmental guidelines to address various emission requirements. This paper presents a methodology to investigate the feasibility of both GHG and NGHG emission reduction in a deregulated electricity market. The proposed model takes into consideration the effect of NGHG emission cost constraints in conjunction with classical GHG emission constraints for the scheduling aspects of GENCO. A profit based self-scheduling problem with conventional fossil fueled generators and renewable energy technologies (RETs) is formulated including emission penalties and avoidance costs of GHG and NGHG emissions, respectively. Thereafter, a set of pareto solutions is evaluated for different possible scheduling scenarios. A simple, effective optimality criteria is also postulated to identify the tradeoff solution. Finally, a sensitivity analysis of various technical, environmental, as well as economic aspects is presented to examine the effect of NGHG consideration and RET inclusion in scheduling. The simulation results are presented and discussed in detail to examine the effect of NGHG consideration in self-scheduling practices of GENCO in the electricity market, thus reflecting the benefits of the proposed approach over classical emission handling approaches.

A demand response aggregator (DRA)-coordinated load scheduling in distribution system with responsive loads is presented. The mutually exclusive scheduling objectives of demand response provider (DRP) and distribution network operator (DNO) are optimally compromised by DRA through incentive-based participation strategies of DRPs. Thus, the proposed methodology is a two-stage optimisation process in which the prioritised schedules (to minimise total electricity charges/cost) of DRP obtained through first-stage optimisation are rescheduled by DRA in the second-stage optimisation considering the system/DNO benefits. The proposed methodology considers the behaviour (risk averse nature, incentive favourable nature etc.) through DRPs registration in various participation strategies. The effectiveness of proposed methodology on techno-economic aspects/attributes is examined using IEEE 34 bus distribution system for different operating conditions. The sensitivity analysis include the effect of load schedules due to change in strategy election by DRPs, change in performance attributes with respect to the penetration levels of responsive loads. The simulation results demonstrate the effectiveness of DRA-coordinated load scheduling over base case with no responsive loads as well as prioritised schedules of responsive loads by DRPs. The optimal compromise between the objectives of DRPs and DNO is observed in terms of system wide benefits and electricity charges/cost of DRPs. The variation of techno-economic aspects differs between DRPs-based schedules and DRA schedules at higher penetration levels.

In this study, an operation window constrained strategic energy management (OWCSEM) for microgrid operational scheduling is examined. The scheduling problem is formulated as cost minimisation/benefit maximisation of distribution resources. The scheduling operation of distributed resources is evaluated for various perspectives such as customer driven, micro grid operator (MGO) driven and MGO driven with utility constraints. The MGO driven scheduling operation considers the distribution network loss minimisation in addition to economic benefits of distributed resources. The loss estimation of distributed network/microgrid is performed using forward-backward sweep algorithm, considering the ill-natured radial network. Thereafter, the optimisation is performed using modified gradient-based search method, considering the linear nature of the problem. The simulation results are presented and discussed for the three scheduling scenarios proposed under operation window constraints. The customer driven scheduling of distributed resources resulted in highest profit among all the scheduling alternatives. On the other hand, the MGO driven scheduling horizon resulted in lower line loss cost component at a compromise of distribution resource benefit. The utility constrained MGO driven scheduling objective attained a compromise solution in terms of loss component and customer benefit. Thus, it is identified as best practice for overall welfare maximisation of microgrid/distribution network.

This paper discusses the modeling and analysis of a carbon capture technology-based resource scheduling/unit commitment (UC) methodology in carbon/emission-constrained environment. In carbon markets, the overall generation from conventional/fossil-fueled thermal plants is constrained over the total cost which is the sum of generation, capture cost/emission avoidance cost. Therefore, in this paper, generalized/uniform performance indices affecting the UC schedule are derived. Also, a commitment/scheduling methodology based on capture and fuel cost is devised rather than the existing penalty cost methodology as in the modernized markets everything was dealt in monetary value. Furthermore, the impact of type of resource used, i.e., coal rank is also considered to evaluate the sensitivity of scheduling decisions and financial influence of carbon capture technology. Along with performance indices, correction factors are also proposed to justify the effect of resource/coal rank in the process of optimal generation allocation. The effect of correction factors and type of coal is observed to be predominant at lower capture efficiencies compared to higher capture efficiency. The effectiveness of the proposed method over the penalty method is reflected in reduced generation cost and emission avoidance cost when compared to the penalty-based methodology.

This paper presents Binary Grey Wolf Optimizer (BGWO) for solving the Unit Commitment Problem (UCP) of power system operation. The optimization method used is a binary variant of the grey wolf optimization (GWO) wherein some modifications have been made as a first attempt to solve UCP. Two generic approaches are examined for the conversion from real-valued to binary transformation. In addition, two variants of each approach are explored. The BGWO finds the optimal combination while lambda-iteration method has been applied for arriving at the solution of their economic load dispatch problem. Heuristic adjustment has been applied to satisfy the constraints of the optimization problem. The performance and efficiency of the proposed method has been checked by applying to three standard IEEE test systems with various dimensions and compared with the real-valued GWO approaches to solve UCP. The results demonstrate the advantage of fast convergence and better quality of the proposed approaches over existing methods.

The multipurpose use of electric vehicles (EVs) makes it a special entity in the transportation sector and in the modern smart grid for use as a reserve service. This paper presents an application ...

The deregulation of electricity markets has transformed the unit commitment and economic dispatch problem in power systems from cost minimization approach to profit maximization approach in which generation company (GENCO)/independent power producer (IPP) would schedule the available generators to maximize the profit for the forecasted prices in day ahead market (DAM). The PBUC is a highly complex optimization problem with equal, in equal and bound constraints which allocates scheduling of thermal generators in energy and reserve markets with no obligation to load and reserve satisfaction. The quality of the solution is important in deciding the commitment status and there by affecting profit incurred by GENCO/IPPs. This paper proposes a binary coded fireworks algorithm through mimicking spectacular display of glorious fireworks explosion in sky. In deregulated market GENCO/IPP has the freedom to schedule its generators in one or more market(s) based on the profit. The proposed algorithm is tested on thermal unit system for different participation scenarios namely with and without reserve market participation. Results demonstrate the superiority of the proposed algorithm in solving PBUC compared to some existing benchmark algorithms in terms of profit and number of iterations.

Global warming and climate change are two key probing issues in the present context. The electricity sector and transportation sector are two principle entities propelling both these issues. Emissions from these two sectors can be offset by switching to greener ways of transportation through the electric vehicle (EV) and renewable energy technologies (RET). Thus, effective scheduling of both resources holds the key to sustainable practice. This paper presents a scheduling scenario-based approach in the smart grid. Problem formulation with dual objective function including both emissions and cost is developed for conventional unit commitment with EV and RET deployment. In this work, the scheduling and commitment problem is solved using the fireworks algorithm which mimics explosion of fireworks in the sky to define search space and the distance between associated sparks to evaluate global minimum. Further, binary coded fireworks algorithm is developed for the proposed scheduling problem in the smart grid. Thereafter, possible scenarios in conventional as well as smart grid are put forward. Following that, the proposed methodology is simulated using a test system with thermal generators.

This article presents an integrated and intelligent price based self-scheduling approach for demand response (DR) aggregators in the wholesale market considering load profile attributes of aggregated loads. The load profile attributes namely utilization and availability factors are derived as a function of load type and temporal characteristics of responsive loads. The proposed integrated approach uses the concept of conventional random willingness factors coupled with load profile attributes to resolve the ambiguity around customer/load behavior. The response of the customer to load profile attributes and the willingness factor is formulated using three different models, namely, linear, exponential, and nonlinear models. Further, to consider the nondeterministic and indistinct behavior of the customer, a fuzzy inference system (FIS) is developed to create a relationship between the load profile attributes and willingness to DR participation cost. Further, an online FIS membership function parameter tuning mechanism is developed to improve the performance of DR aggregator as well as overall day ahead market. The proposed approach is simulated for a combined price based scheduling of the generation company and the DR aggregator with responsive loads spread over various load sectors such as industrial, commercial, residential, agricultural, and the municipal sector. The simulation results of the online intelligent-integrated framework are compared to conventional willingness model, nonfuzzy, and untuned FIS models with and without considering load profile and customer willingness factor. The comparison of the same demonstrates the effectiveness of the proposed online framework in improving the overall and DR aggregator surplus over other approaches.

A binary variant of moth-flame optimizer, namely the binary moth-flame optimizer algorithm (BMFOA) is developed in this chapter and is applied to solve unit commitment (UC) problem in power system operation. The moth-flame algorithm is a bio-inspired optimization algorithm that mimics the traverse navigation mechanism of moth around flames. The navigation mechanism is modelled as a spirally converging approach of moth towards flame. However, the direct application of real-valued moth-flame optimization algorithm (MFOA) to binary matured problems such as UC problem is not possible considering the binary search space attributes. Thus, a binary variant BMFOA is developed via modified sigmoidal transformation of real-valued MFOA. The efficacy of proposed BMFOA is demonstrated through numerical experiments using test systems of different sizes ranging from small-to-medium and large scale. The simulation results are presented, discussed and compared to various existing approaches to solve UC problems. In addition, the statistical significance of BMFOA with respect to other existing approaches is established by performing standard statistical tests such as Friedman, Friedman aligned ranks test, Wilcoxon pairwise test and Quade test. The comparison of statistical test results confirms the statistical significance of proposed BMFOA for solving UC problem of different scales.

This chapter discusses the development and application of an intelligent computational technique called binary whale optimization algorithm (BWOA) and its application to solve the unit commitment (UC) problem. The whale optimization is a heuristic approach that mimics the intelligence associated with hunting and feeding behaviour of whales. The two distinct properties of location updates of whales, namely shrinking approach and spiral update approach are used for optimizing the position of prey. To improvise the real-valued whale optimization algorithm for binary UC problem, update process of whale position is mapped to binary search space using various transfer functions. The binary variants include three sigmoidal transformations and two tangent hyperbolic transformations. The binary variants presented are evaluated using extensive numerical experiments on various test systems and operating conditions. The simulation results are presented and compared to various existing classical/traditional, heuristic and meta-heuristic approaches. In addition, the statistical significance of proposed BWOA approaches among other binary approaches and within themselves is verified using a series of standard statistical tests. The same demonstrates the effectiveness of proposed BWOA to solve UC problem of small, medium and large scale.

This article presents a metaheuristic approach, the binary whale optimization algorithm (BWOA), to solve complex, constrained, non-convex, binary-nature profit-based unit commitment (PBUC) optimization problems of a price-taking generation company (GenCo) in the electricity market. To simulate the binary-nature PBUC problem, the continuous, real-value whale position/location is mapped into binary search space through various transfer functions. This article introduces three variants of BWOA using tangential hyperbolic, inverse tangent (arctan) and sigmoidal transfer functions. The effectiveness of the BWOA approaches is examined in test systems with different market mechanisms, i.e. an energy-only market, and energy and reserve market participation with different reserve payment methods. The simulation results are presented, discussed and compared with other existing approaches. The convergence characteristics, solution quality and consistency of the results across different BWOA variants are discussed. The superiority and statistical significance of the proposed approaches with respect to existing approaches is also presented.

Technological breakthroughs in renewable energy technologies (RETs) enabled them to attain grid parity thereby making them potential contenders for existing conventional resources. To examine the market participation of RETs, this paper formulates a scheduling problem accommodating energy market participation of wind- and solar-independent power producers (IPPs) treating both conventional and RETs as identical entities. Furthermore, constraints pertaining to penetration and curtailments of RETs are restructured. Additionally, an appropriate objective function for profit incurred by conventional resource IPPs through reserve market participation as a function of renewable energy curtailment is also proposed. The proposed concept is simulated with a test system comprising 10 conventional generation units in conjunction with solar photovoltaic (SPV) and wind energy generators (WEG). The simulation results indicate that renewable energy integration and its curtailment limits influence the market participation...

Increasing global warming concerns and depletion of fossil fuels are the major reasons for increase in Electric Vehicles (EVs) adoption in the transportation sector. Transportation electrification is the one of key factors capable of curtailing the pollution levels globally. However, large population of EVs in distribution system can cause negative impacts too. To handle this issue, a two-stage charging framework has been proposed in this paper. In the first stage, EV owners schedule their vehicles to charge according to minimum cost in available period. This information then shear by the load aggregator to the second stage. There incentive based approach is proposed to manage the EVs for technical benefit of distribution system. This study is performed on the 123 bus unbalanced distribution system. The simulation results of various test cases are presented, discussed and compared to demonstrated the effectiveness of the proposed two stage framework.

In the emerging smart grid technologies, resource utilization is diversified and responsive demand became an important tool in availing various benefits. The informed load curtailment is a popular means of demand response aspect with customers being facilitated by financial incentives for offered load reduction. Therefore, augmentation of such responsive loads with unit commitment process can be effective in resource scheduling of smart grid. This paper attempts to develop a binary fireworks algorithm application solve unit commitment problem along with demand response and wind energy. While the wind energy is considered as non schedulable, load curtailment is modelled as schedulable through demand response aggregator. Further, system and thermal unit constraints applicable for wind energy and DR are modified. The proposed methodology is tested on system with coal fired thermal generation, demand response aggregator and wind energy. Various possible scheduling scenarios are laid out for examination. Simulation results are presented and discussed in detail. Results shows the effectiveness of demand response in reducing the operational cost of system operator. Also, the demand response showed betterment when scheduled along with wind energy. The reduction in cost from from traditional thermal unit scheduling is observed to be 6.5% which is best among all the possible scenarios.

Ever increasing thrust on exploration of new and renewable energy resources resulted in remarkable capacity additions of wind energy generators. Now, the challenge that is being faced by electricity markets in accommodating available wind power is with the inheritant uncertainty associated with wind generation. In this paper, such uncertainty is handled by assessing the risk of independent system operator (ISO)/ transmission system operator (TSO). The risk of scheduling uncertain wind energy is modeled as balancing cost for ISO in real time market as a function of expected real time energy bids of conventional/thermal generators. The proposed approach is simulated on IEEE 30 bus system and results are compared with deterministic scheduling approach. The effectiveness of proposed approach for wind energy scheduling in day ahead (DA) market is reflected in reduced cost for system operator. It is also observed that, cost savings for ISO increased with an increase in wind uncertainty. However, the cost effectiveness of wind scheduling has reduced the scheduled wind energy in DA market. Therefore, percentage of savings increases with uncertainty margin in the forecasted wind energy at a cost of reduction in scheduled wind energy in DA market.

Global warming and climate changes propelled many clean technologies across various fields. The electrification of transportation is considered to be one of the key approaches that can curtail the pollution levels prevailing across global fraternity. Electric vehicle (EV) technologies gained substantial importance in this context. The versatility of EV acting as source and sink in a power system projected many challenges. This paper examines one of such challenge i.e., optimal charge-discharge scheduling of EV along with conventional thermal unit commitment (UC). A modified particle swarm optimization (MPSO) is developed to solve the optimal schedule of EV. In addition, a binary version of MPSO is also developed to determine commitment schedule of thermal units. Results confirm the effectiveness of MPSO in solving UC problem compared to some of the benchmark optimization approaches used to solve UC problem.

Ever increasing energy requirements and rising emission levels have been driving the electricity sector towards more sustainable and eco-friendly methods of power generation. Efficiency improvement of photovoltaic (PV) cell through every possible method is in focus to improve the energy economics of solar PV technology. Concentrated solar PV systems are one of the contenders for high efficiency solar PV systems. However, along with power enhancement, other parameters like open circuit voltage, short circuit current and fill factor of the solar cells that are used in the module are subjected to variation with concentrated solar PV systems. For an inverter, along with power input, the maximum voltage and current inputs are equally important for effective operation. The variation of these parameters in non-linear fashion makes mathematical modelling difficult and complex. Therefore, in the present experiment, variations in voltage, current, fill factor and module temperatures are examined using V-Trough concentrating system. From the experimental results, the actual voltage dependence on temperature and intensity is found to be more compared to the theoretical estimation. Interesting variation of photovoltaic current of concentrated module has been observed during evening times. Fill factor and thermal parameter variations like front and back surface temperature with concentration are simulated and presented along with practical results.

The ever increasing demand for energy and rapid raise in global pollution levels has driven the energy generating sectors towards the search for more sustainable and eco-friendly methods to generate it. Solar energy is the best potential candidate to drive the global energy demand as well as to curb the pollution levels in earth. The photo voltaic and solar thermal systems are the widely developed present technologies to extract the solar energy for the use of mankind. The photo voltaic systems are widely employed around the world due to its capability of converting the available solar energy into the electrical energy which is the most flexible energy available currently on earth. But the commercial adoption of photo voltaic systems on a wide scale is still dented by the limitation of their lower efficiencies. Hence the methods for improvement in efficiency is on focus to improve the economics of these systems. Apart from the power enhancement (for improving the efficiency), other parameters like open circuit voltage, short circuit current, fill factor are also important to consider in the technical point of view viz., for inverter & inter connection with grid systems, performance assessment. In this present work the variation in the above mentioned parameters with the provision of V-trough concentrator arrangement for a module under consideration along with back surface cooling provision is analyzed through the day. Fill factor and efficiency variations were simulated and presented along with practical results.

The deregulation and introduction of green technologies are the two key developments took place in electricity sector during the last decade. These developments have brought in several features into the system and increased complexity of system at planning ans well as operation level of power system. This paper deals with one of the key procedure of power system operation i.e., resource scheduling. In this paper, multi-objective aspects of resource scheduling in deregulated environment are examined treating both conventional and renewable energy sources as identical entities as per the market participation is concerned. The scheduling problem is formulated as a multi-objective problem with both economic as well as environmental aspects. The network and conventional/thermal generator constraints are remodelled pertaining to the market participation of renewable energy sources. Also, different scheduling scenarios are generated for analyzing the variation of different system and market parameters. The simulation results confirms the influence of weight factors or the importance assigned to objectives while the same have also affected the network parameters like scheduled renewable energy, reserve offer to market. Therefore, the proposed methodology can help in generation company/ independent system operator (ISO) in the selection of particular scheduling scenario pertaining to imposed compliances on emissions/renewable energy deployment obligations.

The uncertain wind energy handling is a vital aspect of modern power system operational planning with considerable wind penetration. The handling of same is a techno-economic constrained procedure considering its effect on energy and reserve scheduling of whole generation mix. This paper presents a dynamic penalty cost based methodology to solve power system resource scheduling problem with uncertain wind energy, thermal units and responsive loads using binary grey wolf optimizer (BGWO). The proposed methodology considers the effect of uncertain wind energy in terms of total rescheduling cost, total energy balancing cost and total reserve cost. The unit commitment procedure is solved using BGWO and the economic dispatch of committed thermal units alongside the wind energy, responsive load scheduling is solved using Lambda iteration technique. In addition, two different levels of wind uncertainty level are considered to examine the variation techno-economic aspects of proposed methodology. The simulation results are presented and discussed with respect to various performance attributes and the same demonstrate the superior performance of proposed dynamic penalty cost models over existing static cost models.