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The purpose of this paper is to solve economic emission dispatch problem in connection of wind with hydro-thermal units.

The proposed hybrid methodology is the joined execution of both the modified salp swarm optimization algorithm (MSSA) with artificial intelligence technique aided with particle swarm optimization (PSO) technique.

The proposed approach is introduced to figure out the optimal power generated power from the thermal, wind farms and hydro units by minimizing the emission level and cost of generation simultaneously. The best compromise solution of the generation power outputs and related gas emission are subject to the equality and inequality constraints of the system. Here, MSSA is used to generate the optimal combination of thermal generator with the objective of minimum fuel and emission objective function. The proposed method also considers wind speed probability factor via PSO-artificial neural network (ANN) technique and hydro power generation at peak load demand condition to ensure economic utilization.

To validate the advantage of the proposed approach, six- and ten-units thermal systems are studied with fuel and emission cost. For minimizing the fuel and emission cost of the thermal system with the predicted wind speed factor, the proposed approach is used. The proposed approach is actualized in MATLAB/Simulink, and the results are examined with considering generation units and compared with various solution techniques. The comparison reveals the closeness of the proposed approach and proclaims its capability for handling multi-objective optimization problems of power systems.

With the inclusion of significant wind power into the power system, the unit commitment (UC) has become challenging due to frequent variations in wind power, load and requirement of reserves with sufficient ramp rate. The pumped storage units with lesser startup time and cost can take care of these sudden variations and reduce their impact on power system operation. The aim of this paper is to provide a solution model for UC problem in a hybrid power system.

The model developed has been implemented through GAMS optimization tool with CONOPT solver. The model has been called into MATLAB platform by using GAMS‐MATLAB interfacing to obtain solutions.

The model provides an efficient operating schedule for conventional units and pumped storage units to minimize operating cost and emission. The effects of wind power and load profiles on emission, operating cost and reserve with enough ramping capabilities have been minimized with the use of pumped storage unit. The commitment schedule of thermal and pumped storage units have been obtained with significant wind power integrated into the system for best cost commitment (BCC) and for a combined objective of cost and emission minimization.

This paper finds that the operating cost and emission in a commitment problem can be reduced significantly during variable wind and load conditions in a hybrid system. The model proposed provides operational schedules of conventional and pumped storage units with variable wind power and load conditions throughout operating horizon. The coordinated optimization approach has been implemented on a hybrid system with IEEE‐30 bus system.

The purpose of this paper is to examine the feasibility of an ideal power network that combines many different renewable energy technologies such as wind power, concentrated solar power (CSP) and hydroelectric power. This paper emphasizes in finding the benefits arising from hydrothermal coordination compared to the non-regulated integration of the hydroelectric units, as well as the benefits from the integration of wind power and CSP.

Artificial Neural Networks were used to estimate wind power output. As for the CSP system, a three-tier architecture which includes a solar field, a transmission-storage system and a production unit was used. Each one of those separate sections is analyzed and the process is modeled. As for the hydroelectric plant, the knowledge of the water’s flow rated has helped estimating the power output, taking into account the technical restrictions and losses during transmission. Also, the economic dispatch problem was solved by using artificial intelligence methods.

Hydrothermal coordination leads to greater thermal participation reduction and cost reduction than a non-regulated integration of the hydrothermal unit. The latter is independent from the degree of integration of the other renewable sources (wind power, CSP).

Hydrothermal coordination in a power system which includes thermal units and CSP for cost and emissions reduction.

This study aims to introduce a methodology for optimal allocation of spinning reserves taking into account load, wind and solar generation by application of the univariate and bivariate parametric models, conventional intra and inter-zonal spinning reserve capacity as well as demand response through utilization of capacity outage probability tables and the equivalent assisting unit approach.

The method uses a novel approach to model wind power generation using the bivariate Farlie–Gumbel–Morgenstern probability density function (PDF). The study also uses the Bayesian network (BN) algorithm to perform the adjustment of spinning reserve allocation, based on the actual unit commitment of the previous hours.

The results show that the utilization of bivariate wind prediction model along with reserve allocation adjustment algorithm improve reliability of the power grid by 2.66% and reduce the total system operating costs by 1.12%.

The method uses a novel approach to model wind power generation using the bivariate Farlie–Gumbel–Morgenstern PDF. The study also uses the BN algorithm to perform the adjustment of spinning reserve allocation, based on the actual unit commitment of the previous hours.

The purpose of this paper is to investigate the gaming equilibrium among fossil‐fueled generation companies (GenCos), wind generation companies, the grid company and customers participating in an emission trading (ET) market and the day‐ahead electricity market.

The complementarity method is used in this work to obtain the Nash equilibrium. By combining the Karush‐Kuhn‐Tucker (KKT) conditions of each kind of market participants with market clearing and consistency conditions, a mixed linear complementarity problem could be established.

Simulation results show that: the enforcement of ET could increase the share of generation outputs of wind generation units, and decrease the emissions from fossil‐fueled generation units; the bilateral contracts between GenCos and customers could limit the ability of exercising market power by GenCos; and when the emissions allowances allocated by the government shrink, the price of emissions allowance will increase and as the result the dispatching order of fossil‐fueled generation units will change, and the shares of generation outputs from wind generation units and combined‐cycle gas turbines increase. However, it should be mentioned that because the cost of wind generation is still very high, the increase of the share from wind generation units in the electricity market should mainly rely on cost reduction rather than the enforcement of ET.

The original contribution and the value of this study lie in developing a model framework to explore the gaming equilibrium that thermal and wind generating plants both play in the emissions trading environment and electricity market.

Generation scheduling (GS) is the most prominent and hard-hitting problem in the electrical power industry especially in an integrated power system. Countless techniques have been used so far to solve this GS problem for proper functioning of the units in the power system to dispatch the load economically to consumers at once. Therefore, this work aims to study for the best possible function of integrated power plants to obtain the most favourable solution to the GS problem.

An appropriate method works in a proper way and assures to give the best solution to the GS problem. The finest function of incorporated power plants should be mathematically devised as a problem and via that the aim of the GS problem to minimize the total fuel cost subject to different constraints will be achieved. In this research work, the latest meta-heuristic and swarm intelligence-based technique called grey wolf optimization (GWO) technique is used as an optimization tool that will work along with the formulated problem for correct scheduling of generating units and thus achieve the objective function.

The recommended GWO technique provides the best feasible solution which is optimal in its performance for different test cases in the GS problem of integrated power plant. It is further found that the obtained solutions using GWO method are better than the former reports of other traditional methods in terms of solution excellence. The GWO method is found to be unique in its performance and having superior computational efficiency.

Decision making is significant for effective operation of integrated power plants in an electrical power system. The recommended tactic implements a modern meta-heuristic procedure that is applied to diverse test systems. The method that is proposed is efficient in providing the best solutions of solving GS problems. The suggested method surpasses the early techniques by offering the most excellent feasible solutions. Thus, it is obvious that the proposed method may be the appropriate substitute to attain the optimal operation of GS problem.

Renewable energy sources are discontinuous and infrequent in nature, and it is tough to predict them in general. Further, integrating renewable energy source-based plants with the conventional plant is extremely difficult to operate and maintain. Operation of integrated power system is full of challenges and complications. To handle those complications and challenges, the GWO algorithm is suggested for solving the GS problem and thus obtain the optimal solution in integrated power systems by considering the reserve requirement, load balance, equality and inequality constraints.

The proposed system should be further tested on diverse test systems to evaluate its performance in solving a GS problem and the results should be compared. Computation results reveal that the proposed GWO method is efficient in attaining best solution in GS problem. Further, its performance is effectively established by comparing the result obtained by GWO with other traditional methods.

Technological development has not only helped in effective integration of renewable sources but also made it possible for consumers to participate in system operation. Different market players are coming up in the electricity market, microgrid being one of them. Thus, this paper aims to investigate consumers’ role in the dispatch of a microgrid system that has a hybrid market structure under varied system conditions.

The mathematical model developed has been solved by the CONOPT solver in the GAMS optimization tool. GAMS-MATLAB interfacing is done to obtain solutions.

The problem formulated shows the effect of consumers in dispatch and overall operational cost. Consumers’ participation has been proposed through a quadratic cost function. The system operation under pool and bilateral contracts has been investigated. It shows that proper incentives to the consumers can help in reduction and effective management of the demand, carbon emission and overall system operational cost.

This paper considers the hybrid market structure to find the load dispatch in a microgrid system. The participation of consumers in the microgrid system has been implemented considering variations in wind power, solar power and load. The power exchange between the grid and microgrid system has been modeled showing the contribution of the consumers in system operation.

The purpose of this paper is to discuss how wind farms attract wind turbine manufacturers to get involved in wind turbines’ maintenance service with revenue sharing contract of bundled service under which the background of operation and maintenance (O&M) aftermarket of wind turbine exists. The authors also try to extend the results to the application of product plus service business mode on large-scale equipment O&M service. At present, Chinese wind power industry is suffering from production capacity redundancy. The profit levels for both wind farm and wind turbine manufacturers are relatively low. It is significant for Chinese wind power industry development to coordinate the supply chain of wind power in order to reduce O&M costs and increase revenues.

The present paper discusses product plus aftermarket service contract design on the background of closed-loop product service chain and uncertain equipment demand using revenue sharing contract model.

If centralized decision making is assumed, the authors find that the wind turbine order increases as the aftermarket service effort level and aftermarket service profit increase; aftermarket service effort level is positively correlative to the service efficiency. On the other hand, if decentralized decision making is assumed, the wind turbine order increases as share of the aftermarket service chain by manufacturer to wind farm increases and share of product supply chain by wind farm to manufacturer decreases. The optimal effort level of wind farm increases as the share of aftermarket service chain increases while the optimal effort level of the manufacturer is a concave function of share of aftermarket service chain if service quality linear correlates with effort level. Meanwhile, the authors find that the revenues of the product supply chain and aftermarket service chain have a concave relationship. This relationship is not affected by the format of relationship between service quality and effort level (linear or exponential).

The results could potentially be used to provide the wind turbine manufacturer with a greater profit space and satisfy wind farm’s equipment maintenance demand at the same time. It can also guide the practice of revenue sharing in the aftermarket service and manufacturing servitization.

In this model, the authors assumed that both the forward revenue sharing of power generation by wind farm to manufacturer and the backward revenue sharing of maintenance service by the manufacturer to wind farm exist in closed-loop product service chain. Then the authors discussed channel coordination of such cross-revenue sharing contract.

Taking the perspective of a start-up company, the purpose of this paper is to analyse resource renewal in heavy business networks.

The theoretical framework is based on the Industrial Network Approach and, especially, the resource interaction framework, business network settings and studies of starting up in business networks. The basis for the paper is a case study of a start-up in the Swedish wind energy context.

Resource renewal in this case means replacing one resource, having implications for the resource interfaces in the three business network settings.

The paper contributes to the area of studies of starting up in business networks by identifying a distinct form of resource renewal in heavy business networks enabled by development of resource interfaces in three business network settings.

Managers in start-ups as well as established firms need to interact to create and develop the resource interfaces that are needed to achieve resource renewal. Resource renewal not only is in the hands of start-ups but also requires interactive resource development with various collaboration partners.

This study takes a start-up’s perspective to resource renewal of heavy business networks and analyses heaviness based on resource interfaces in three business network settings.

This study aims to form a new concept of power-to-gas-based virtual power plant (GVPP) and propose a low-carbon economic scheduling optimization model for GVPP considering carbon emission trading.

In view of the strong uncertainty of wind power and photovoltaic power generation in GVPP, the information gap decision theory (IGDT) is used to measure the uncertainty tolerance threshold under different expected target deviations of the decision-makers. To verify the feasibility and effectiveness of the proposed model, nine-node energy hub was selected as the simulation system.

GVPP can coordinate and optimize the output of electricity-to-gas and gas turbines according to the difference in gas and electricity prices in the electricity market and the natural gas market at different times. The IGDT method can be used to describe the impact of wind and solar uncertainty in GVPP. Carbon emission rights trading can increase the operating space of power to gas (P2G) and reduce the operating cost of GVPP.

This study considers the electrical conversion and spatio-temporal calming characteristics of P2G, integrates it with VPP into GVPP and uses the IGDT method to describe the impact of wind and solar uncertainty and then proposes a GVPP near-zero carbon random scheduling optimization model based on IGDT.

This study designed a novel structure of the GVPP integrating P2G, gas storage device into the VPP and proposed a basic near-zero carbon scheduling optimization model for GVPP under the optimization goal of minimizing operating costs. At last, this study constructed a stochastic scheduling optimization model for GVPP.