Uncertainty management in multiobjective hydro-thermal self-scheduling under emission considerations

In this paper, a stochastic multiobjective framework is proposed for a day-ahead short-term Hydro Thermal Self-Scheduling (HTSS) problem for joint energy and reserve markets. An efficient linear formulations are introduced in this paper to deal with the nonlinearity of original problem due to the dynamic ramp rate limits, prohibited operating zones, operating services of thermal plants, multi-head power discharge characteristics of hydro generating units and spillage of reservoirs. Besides, system uncertainties including the generating units\u27 contingencies and price uncertainty are explicitly considered in the stochastic market clearing scheme. For the stochastic modeling of probable multiobjective optimization scenarios, a lattice Monte Carlo simulation has been adopted to have a better coverage of the system uncertainty spectrum. Consequently, the resulting multiobjective optimization scenarios should concurrently optimize competing objective functions including GENeration COmpany\u27s (GENCO\u27s) profit maximization and thermal units\u27 emission minimization. Accordingly, the ε-constraint method is used to solve the multiobjective optimization problem and generate the Pareto set. Then, a fuzzy satisfying method is employed to choose the most preferred solution among all Pareto optimal solutions. The performance of the presented method is verified in different case studies. The results obtained from ε-constraint method is compared with those reported by weighted sum method, evolutionary programming-based interactive Fuzzy satisfying method, differential evolution, quantum-behaved particle swarm optimization and hybrid multi-objective cultural algorithm, verifying the superiority of the proposed approach

Multi-objective, multi-year dynamic generation and transmission expansion planning- renewable energy sources integration for Iran's National Power Grid

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Multiobjective optimization of a thermoacoustic regenerator

Abstract: This paper presents a new mathematical approach for optimizing the geometry of a thermoacoustic regenerator, aimed at producing efficient thermoacoustic engines. Optimal set of parameters describing the device are computed for a chosen thermoacoustic couple to illustrate this approach. Hence, a non-linear multiobjective problem is formulated in GAMS and solved using Lindoglobal solver. Lexicographic optimization is presented as an alternative optimization technique to the common used weighting methods. This approach establishes a hierarchical order among all the optimization objectives instead of giving them a specific (and most of the time, arbitrary) weight. In this work, the optimization criteria are chosen as work output, viscous resistance as well as thermal losses that are typically disregarded when modeling the device. A practical example is given, in a hypothetical scenario, showing how the proposed optimization technique may help thermoacoustic regenerator designers to identify Pareto optimal solutions when dealing with geometric parameters

VPP Self-Scheduling Strategy Using Multi-Horizon IGDT, Enhanced Normalized Normal Constraint, and Bi-Directional Decision-Making Approach

This paper presents a new robust self-scheduling strategy for virtual power plants (VPPs) considering the uncer-tainty sources of electricity prices, wind generations, and loads. Multi-horizon information-gap decision theory (MH-IGDT) as a non-deterministic and non-probabilistic uncertainty modeling framework is proposed here to specifically model the uncertainty sources considering their various uncertainty horizons. Since each uncertain parameter tends to optimize its uncertainty horizon competitively for a particular value of the uncertainty budget, the proposed MH-IGDT model is formulated as a multi-objective op-timization problem. To solve this multi-objective problem, en-hanced normalized normal constraint (ENNC) method is pre-sented, which can obtain efficient uniformly-distributed Pareto optimal solutions. The proposed ENNC includes augmented nor-malized normal constraint method and lexicographic optimiza-tion technique to enhance the search performance in the objective space. To address the unsolved issue of being risk-averse or risk-seeker for a VPP in the market, a bi-directional decision-making approach is presented. This decision maker comprises an ex-ante performance evaluation method and a forward-backward dy-namic programming approach to hourly find the best Pareto so-lution within the generated risk-averse and risk-seeker Pareto frontiers. Simulation results of the proposed self-scheduling strat-egy are presented for a VPP including dispatchable/non-dispatch-able units, storages, and loads

Lexicographic Multi-objective Optimisation of Hybrid Power Generation Systems for Communities in Non-Interconnected Zones

Energy supply applications for populations in isolated areas have great importance in reducing poverty and environmental impacts. However, rural zones not connected to power grids require more studies related to select low cost and efficient technologies suitable to each size of community. This paper presents a lexicographic multi-objective optimisation model (LMOM) to select the best renewable energy technologies (solar and wind) of hybrid power generation systems (HPGSs) for communities located in non-interconnected zones. The model prioritises objective functions such as the generation cost, emissions of carbon dioxide (CO2) and energy consumption in ton-equivalent petroleum (TEP). In addition, the model considers constraints related to the production cost, environmental sustainability, environmental conditions and reliability of the system. The results show that the model provides sustainable HPGSs adapted to the size of each community and the best renewable energy technologies are the central receiver system and the wind turbine with a 20-metre tower. Keywords: Renewable Energy, Rural Communities, Multi-objective Optimization JEL Classifications: C61, Q42 DOI: https://doi.org/10.32479/ijeep.757

A robust optimization approach for active and reactive power management in smart distribution networks using electric vehicles

YesThis paper presents a robust framework for active and reactive power management in distribution networks using electric vehicles (EVs). The method simultaneously minimizes the energy cost and the voltage deviation subject to network and EVs constraints. The uncertainties related to active and reactive loads, required energy to charge EV batteries, charge rate of batteries and charger capacity of EVs are modeled using deterministic uncertainty sets. Firstly, based on duality theory, the max min form of the model is converted to a max form. Secondly, Benders decomposition is employed to solve the problem. The effectiveness of the proposed method is demonstrated with a 33-bus distribution network

Impact of environmental concerns on the capacity-pricing problem in the car rental business

Redefinition of a mathematical model implemented to solve the problem of capacity-pricing in car rental. To this model, environmental aspects were studied and added in order to study their influence on the results of that same model. In order to optimize this multi objective function some methodologies are applied such as a weighted sum or the epsilon constraint

Extension of zigzag search algorithms for power system multi-objective optimization

The work presented in this thesis focuses on the application and extension the zigzag search algorithms in power systems. The zigzag search method is a multi-objective algorithm which has recently been applied in multiple engineering fields, such as oil well replacement, with fast computational time and accurate results.Multi-objective optimization algorithms in power systems have been investigated for years. Most of the literatures focus on evolutionary algorithms (EA) such as a non-dominated sorting genetic algorithm (NSGA) or multi-objective particle swarm optimization (MOPSO) for their simplicity and ease of implementation. However, there have been several issues regarding the evolutionary algorithm (EA). For example, the computational time of EA is significant and the parameter configurations are complicated. Other approaches mainly reply on the weight sum method by lumping together different objective functions to form a new single objective function; however, the priority is hard to determine and the characteristic between different objectives may be lost.In order to improve the performance of power system multi-objective optimization problems, this thesis will first introduce the zigzag search algorithm. Second, by modifying the classic zigzag search algorithm, the zigzag interior point method and zigzag genetic algorithm method will both be proposed to broaden the applications of the classic zigzag search method. Also, in order to provide a systematic method for step-size configuration, a zigzag search method with adaptive step-size will be proposed. Thirdly, all algorithms will be applied to several practical power system multi-objective problems to demonstrate their practicability and effectiveness.The case study will be carried out on a modified IEEE 30-bus system and the IEEE 118-bus system. A comparison will be made with classic multiobjective algorithms which have been widely applied in power systems to demonstrate the effectiveness and efficiency of the proposed zigzag search methods

Reserve services provision by demand side resources in systems with high renewables penetration using stochastic optimization

It is widely recognized that renewable energy sources are likely to represent a significant portion of the production mix in many power systems around the world, a trend expected to be increasingly followed in the coming years due to environmental and economic reasons. Among the different endogenous renewable sources that may be used in order to achieve reductions in the carbon footprint related to the electricity sector and increase the economic efficiency of the generation mix, wind power generation has been one of the most popular options. However, despite the potential benefits that arise from the integration of these resources in the power system, their large-scale integration leads to additional problems due to the fact that their production is highly volatile. As a result, apart from the typical sources of uncertainty that the System Operators have to face, such as system contingencies and intra-hour load deviations, through the deployment of sufficient levels of reserve generation, additional reserves must be kept in order to maintain the balance between the generation and the consumption. Furthermore, a series of other problems arise, such as efficiency loss because of ramping of conventional units, environmental costs because of increased emissions due to suboptimal unit commitment and dispatch and more costly system operation and maintenance. Recently, it has been recognized that apart from the generation side, several types of loads may be deployed in order to provide system services and especially, different types of reserves, through demand response. The contribution of demand side reserves to accommodate higher levels of wind power generation penetration is likely to be of substantial importance in the future and therefore, the integration of these resources in the system operations needs to be thoroughly studied. This thesis deals with the aspects of demand response as regards the integration of wind power generation in the power system. First, a mapping of the current status of demand response internationally is attempted, followed also by a discussion concerning the opportunities, the benefits and the barriers to the widespread adoption of demand side resources. Then, several joint energy and reserve market structures are developed which explicitly incorporate demand side resources that may contribute to energy and reserve services. Two-stage stochastic programming is employed in order to capture the uncertainty of wind power generation. Moreover, several aspects of demand response are considered such as the capability of providing contingency and load following reserves, the appropriate modeling of industrial consumer processes load and the load recovery effect. Finally, this thesis investigates the effect of demand side resources on the risk that is associated with the decisions of the System Operator through appropriate risk management techniques, proposing also a novel methodology of handling risk as an alternative to the commonly used technique.It is widely recognized that renewable energy sources are likely to represent a significant portion of the production mix in many power systems around the world, a trend expected to be increasingly followed in the coming years due to environmental and economic reasons. Among the different endogenous renewable sources that may be used in order to achieve reductions in the carbon footprint related to the electricity sector and increase the economic efficiency of the generation mix, wind power generation has been one of the most popular options. However, despite the potential benefits that arise from the integration of these resources in the power system, their large-scale integration leads to additional problems due to the fact that their production is highly volatile. As a result, apart from the typical sources of uncertainty that the System Operators have to face, such as system contingencies and intra-hour load deviations, through the deployment of sufficient levels of reserve generation, additional reserves must be kept in order to maintain the balance between the generation and the consumption. Furthermore, a series of other problems arise, such as efficiency loss because of ramping of conventional units, environmental costs because of increased emissions due to suboptimal unit commitment and dispatch and more costly system operation and maintenance. Recently, it has been recognized that apart from the generation side, several types of loads may be deployed in order to provide system services and especially, different types of reserves, through demand response. The contribution of demand side reserves to accommodate higher levels of wind power generation penetration is likely to be of substantial importance in the future and therefore, the integration of these resources in the system operations needs to be thoroughly studied. This thesis deals with the aspects of demand response as regards the integration of wind power generation in the power system. First, a mapping of the current status of demand response internationally is attempted, followed also by a discussion concerning the opportunities, the benefits and the barriers to the widespread adoption of demand side resources. Then, several joint energy and reserve market structures are developed which explicitly incorporate demand side resources that may contribute to energy and reserve services. Two-stage stochastic programming is employed in order to capture the uncertainty of wind power generation. Moreover, several aspects of demand response are considered such as the capability of providing contingency and load following reserves, the appropriate modeling of industrial consumer processes load and the load recovery effect. Finally, this thesis investigates the effect of demand side resources on the risk that is associated with the decisions of the System Operator through appropriate risk management techniques, proposing also a novel methodology of handling risk as an alternative to the commonly used technique