scholarly journals Optimizing Day-Ahead Electricity Market Prices: Increasing the Total Surplus for Energy Exchange Istanbul

2020 ◽  
Vol 22 (4) ◽  
pp. 700-716 ◽  
Author(s):  
Kursad Derinkuyu ◽  
Fehmi Tanrisever ◽  
Nermin Kurt ◽  
Gokhan Ceyhan
Keyword(s):  
Genetic Algorithms ◽  
Tabu Search ◽  
Social Welfare ◽  
Electricity Market ◽  
Energy Exchange ◽  
Heuristic Algorithms ◽  
Optimization Techniques ◽  
Problem Definition ◽  
Combinatorial Auction ◽  
Mixed Integer

Problem definition: We design a combinatorial auction to clear the Turkish day-ahead electricity market, and we develop effective tabu search and genetic algorithms to solve the problem of matching bidders and maximizing social welfare within a reasonable amount of time for practical purposes. Academic/practical relevance: A double-sided blind combinatorial auction is used to determine electricity prices for day-ahead markets in Europe. Considering the integer requirements associated with market participants’ bids and the nonlinear social welfare objective, a complicated problem arises. In Turkey, the total number of bids reaches 15,000, and this large problem needs to be solved within minutes every day. Given the practical time limit, solving this problem with standard optimization packages is not guaranteed, and therefore, heuristic algorithms are needed to quickly obtain a high-quality solution. Methodology: We use nonlinear mixed-integer programming and tabu search and genetic algorithms. We analyze the performance of our algorithms by comparing them with solutions commercially available to the market operator. Results: We provide structural results to reduce the problem size and then develop customized heuristics by exploiting the problem structure in the day-ahead market. Our algorithms are guaranteed to generate a feasible solution, and Energy Exchange Istanbul has been using them since June 2016, increasing its surplus by 448,418 Turkish liras (US$128,119) per day and 163,672,570 Turkish liras (US$46,763,591) per year, on average. We also establish that genetic algorithms work better than tabu search for the Turkish day-ahead market. Managerial implications: We deliver a practical tool using innovative optimization techniques to clear the Turkish day-ahead electricity market. We also modify our model to handle similar European day-ahead markets and show that performances of our heuristics are robust under different auction designs.

scholarly journals Analysis of Transmission Cost Allocation Strategies with Reliability for Deregulated Systems

2019 ◽  
Vol 8 (9S3) ◽  
pp. 391-397
Keyword(s):  
Genetic Algorithms ◽  
Tabu Search ◽  
Cost Allocation ◽  
Optimization Techniques ◽  
Transmission Cost ◽  
The Cost ◽  
Allocation Strategies

This paper presents a learning review of various strategies related to the improvement of the reliability for the deregulated system, for instance, Genetic Algorithms (GA), Tabu Search (TS), heuristic calculations and system based techniques. These methodologies were produced for advancing reliability as either software or hardware exclusively. Besides, the cost segments related with limit utilize and reliability advantage charges are resolved and various optimization techniques are acknowledged of action of the goal work

The Discovery of Interesting Nuggets Using Heuristic Techniques

Data Mining ◽  
2011 ◽  
pp. 48-71 ◽  
Author(s):  
Beatriz de la Iglesia ◽  
Victor J. Rayward-Smith
Keyword(s):  
Data Mining ◽  
Genetic Algorithms ◽  
Simulated Annealing ◽  
Tabu Search ◽  
Heuristic Algorithms ◽  
Partial Ordering ◽  
Knowledge Discovery In Databases ◽  
Evaluation Function ◽  
Interactive Process ◽  
Partial Classification

Knowledge Discovery in Databases (KDD) is an iterative and interactive process involving many steps (Debuse, de la Iglesia, Howard & Rayward-Smith, 2000). Data mining (DM) is defined as one of the steps in the KDD process. According to Fayyad, Piatetsky-Shapiro, Smyth and Uthurusamy (1996), there are various data mining tasks including: classification, clustering, regression, summarisation, dependency modeling, and change and deviation detection. However, there is a very important data mining problem identified previously by Riddle, Segal and Etzioni (1994) and very relevant in the context of commercial databases, which is not properly addressed by any of those tasks: nugget discovery. This task has also been identified as partial classification (Ali, Manganaris & Srikant, 1997). Nugget discovery can be defined as the search for relatively rare, but potentially important, patterns or anomalies relating to some pre-determined class or classes. Patterns of this type are called nuggets. This chapter will present and justify the use of heuristic algorithms, namely Genetic Algorithms (GAs), Simulated Annealing (SA) and Tabu Search (TS), on the data mining task of nugget discovery. First, the concept of nugget discovery will be introduced. Then the concept of the interest of a nugget will be discussed. The necessary properties of an interest measure for nugget discovery will be presented. This will include a partial ordering of nuggets based on those properties. Some of the existing measures for nugget discovery will be reviewed in light of the properties established, and it will be shown that they do not display the required properties. A suitable evaluation function for nugget discovery, the fitness measure, will then be discussed and justified according to the required properties.

Time Scheduling of Transit Systems With Transfer Considerations Using Genetic Algorithms

1998 ◽  
Vol 6 (1) ◽  
pp. 1-24 ◽  
Author(s):  
Kalyanmoy Deb ◽  
Partha Chakroborty
Keyword(s):  
Genetic Algorithms ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Integer Optimization ◽  
Transit System ◽  
Decision Variables ◽  
Transit Scheduling ◽  
Algorithmic Approaches

Scheduling of a bus transit system must be formulated as an optimization problem, if the level of service to passengers is to be maximized within the available resources. In this paper, we present a formulation of a transit system scheduling problem with the objective of minimizing the overall waiting time of transferring and nontransferring passengers while satisfying a number of resource- and service-related constraints. It is observed that the number of variables and constraints for even a simple transit system (a single bus station with three routes) is too large to tackle using classical mixed-integer optimization techniques. The paper shows that genetic algorithms (GAs) are ideal for these problems, mainly because they (i) naturally handle binary variables, thereby taking care of transfer decision variables, which constitute the majority of the decision variables in the transit scheduling problem; and (ii) allow procedure-based declarations, thereby allowing complex algorithmic approaches (involving if then-else conditions) to be handled easily. The paper also shows how easily the same GA procedure with minimal modifications can handle a number of other more pragmatic extensions to the simple transit scheduling problem: buses with limited capacity, buses that do not arrive exactly as per scheduled times, and a multiple-station transit system having common routes among bus stations. Simulation results show the success of GAs in all these problems and suggest the application of GAs in more complex scheduling problems.

scholarly journals Investment Incentives in Competitive Electricity Markets

2018 ◽  
Vol 8 (10) ◽  
pp. 1978 ◽  
Author(s):  
Jaber Valinejad ◽  
Taghi Barforoshi ◽  
Mousa Marzband ◽  
Edris Pouresmaeil ◽  
Radu Godina ◽  
...  
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Planning Horizon ◽  
Optimization Method ◽  
Design Criterion ◽  
Mixed Integer ◽  
Investment Incentives ◽  
Mixed Integer Linear Program ◽  
Interconnected Power System ◽  
The Impact

This paper presents the analysis of a novel framework of study and the impact of different market design criterion for the generation expansion planning (GEP) in competitive electricity market incentives, under variable uncertainties in a single year horizon. As investment incentives conventionally consist of firm contracts and capacity payments, in this study, the electricity generation investment problem is considered from a strategic generation company (GENCO) ′ s perspective, modelled as a bi-level optimization method. The first-level includes decision steps related to investment incentives to maximize the total profit in the planning horizon. The second-level includes optimization steps focusing on maximizing social welfare when the electricity market is regulated for the current horizon. In addition, variable uncertainties, on offering and investment, are modelled using set of different scenarios. The bi-level optimization problem is then converted to a single-level problem and then represented as a mixed integer linear program (MILP) after linearization. The efficiency of the proposed framework is assessed on the MAZANDARAN regional electric company (MREC) transmission network, integral to IRAN interconnected power system for both elastic and inelastic demands. Simulations show the significance of optimizing the firm contract and the capacity payment that encourages the generation investment for peak technology and improves long-term stability of electricity markets.

Modeling and Solution Techniques Used for Hydro Generation Scheduling

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1392 ◽  
Author(s):  
Iram Parvez ◽  
JianJian Shen ◽  
Mehran Khan ◽  
Chuntian Cheng
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Programming ◽  
Stochastic Programming ◽  
Short Interval ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
Fuzzy Logics ◽  
Spinning Reserve ◽  
Lagrange Relaxation ◽  
Generation Scheduling

The hydro generation scheduling problem has a unit commitment sub-problem which deals with start-up/shut-down costs related hydropower units. Hydro power is the only renewable energy source for many countries, so there is a need to find better methods which give optimal hydro scheduling. In this paper, the different optimization techniques like lagrange relaxation, augmented lagrange relaxation, mixed integer programming methods, heuristic methods like genetic algorithm, fuzzy logics, nonlinear approach, stochastic programming and dynamic programming techniques are discussed. The lagrange relaxation approach deals with constraints of pumped storage hydro plants and gives efficient results. Dynamic programming handles simple constraints and it is easily adaptable but its major drawback is curse of dimensionality. However, the mixed integer nonlinear programming, mixed integer linear programming, sequential lagrange and non-linear approach deals with network constraints and head sensitive cascaded hydropower plants. The stochastic programming, fuzzy logics and simulated annealing is helpful in satisfying the ramping rate, spinning reserve and power balance constraints. Genetic algorithm has the ability to obtain the results in a short interval. Fuzzy logic never needs a mathematical formulation but it is very complex. Future work is also suggested.

Implications of Price Matching in Supply Chain Negotiation

2021 ◽  
Author(s):  
Weixin Shang ◽  
Gangshu (George) Cai
Keyword(s):  
Supply Chain ◽  
Social Welfare ◽  
Wholesale Price ◽  
Consumer Welfare ◽  
Problem Definition ◽  
Nash Bargaining ◽  
Price Matching ◽  
The Impact ◽  
Multiple Buyers ◽  
Wholesale Pricing

Problem definition: Few papers have explored the impact of price matching negotiation (PM), in which a channel matches its price with the resulting wholesale price bargained by another channel, on firms’ performances, consumer welfare, and social welfare, with and without supply chain coordination. Academic/practical relevance: Negotiation has been widely seen in determining both uniform and discriminatory wholesale prices, which affect outcomes of competitive supply chain practices. Methodology: To characterize the PM mechanism, we use game theory and Nash bargaining theory to compare PM with simultaneous negotiation (SN) through a common-seller two-buyer differentiated Bertrand competition model. Results: Our analysis reveals that PM can benefit the seller but hurt all buyers, which is at odds with some fair wholesale pricing clauses intending to protect buyers. Under coordination with side payments, however, all firms can conditionally benefit more from PM than from SN. Despite firms’ gains, PM leads to less consumer utility and social welfare compared with SN, unless the second buyer in PM is considerably less powerful than the first buyer. Coordination further worsens PM’s negative impact on consumer utility and social welfare. Moreover, the existence of a spot market can increase the wholesale price in PM, hurting buyers, consumers, and society. Furthermore, the qualitative results about PM remain robust under an alternative disagreement point for PM, multiple buyers, and other extensions. Managerial implications: This paper delivers insights on when price matching in supply chain wholesale price negotiation can benefit a seller, buyers, consumers, and society in a variety of scenarios. It advocates how managers can use PM to their own advantages and provides rationale to decision makers for policy regulations regarding wholesale pricing.

The Effects of Patent Extension and Take-Back Regulation on Green Pharmacy

2021 ◽  
Author(s):  
Tianqin Shi ◽  
Nicholas C. Petruzzi ◽  
Dilip Chhajed
Keyword(s):  
Pharmaceutical Industry ◽  
Social Welfare ◽  
Technical Information ◽  
Problem Definition ◽  
Theoretic Model ◽  
Innovative Company ◽  
Take Back ◽  
Starting Point ◽  
European Environmental Agency ◽  
Managerial Implications

Problem definition: The eco-toxicity arising from unused pharmaceuticals has regulators advocating the benign design concept of “green pharmacy,” but high research and development expenses can be prohibitive. We therefore examine the impacts of two regulatory mechanisms, patent extension and take-back regulation, on inducing drug manufacturers to go green. Academic/practical relevance: One incentive suggested by the European Environmental Agency is a patent extension for a company that redesigns its already patented pharmaceutical to be more environmentally friendly. This incentive can encourage both the development of degradable drugs and the disclosure of technical information. Yet, it is unclear how effective the extension would be in inducing green pharmacy and in maximizing social welfare. Methodology: We develop a game-theoretic model in which an innovative company collects monopoly profits for a patented pharmaceutical but faces competition from a generic rival after the patent expires. A social-welfare-maximizing regulator is the Stackelberg leader. The regulator leads by offering a patent extension to the innovative company while also imposing take-back regulation on the pharmaceutical industry. Then the two-profit maximizing companies respond by setting drug prices and choosing whether to invest in green pharmacy. Results: The regulator’s optimal patent extension offer can induce green pharmacy but only if the offer exceeds a threshold length that depends on the degree of product differentiation present in the pharmaceutical industry. The regulator’s correspondingly optimal take-back regulation generally prescribes a required collection rate that decreases as its optimal patent extension offer increases, and vice versa. Managerial implications: By isolating green pharmacy as a potential target to address pharmaceutical eco-toxicity at its source, the regulatory policy that we consider, which combines the incentive inherent in earning a patent extension on the one hand with the penalty inherent in complying with take-back regulation on the other hand, serves as a useful starting point for policymakers to optimally balance economic welfare considerations with environmental stewardship considerations.

scholarly journals Comparative Assessment of Priority Listing and Mixed Integer Linear Programming Unit Commitment Methods for Non-Interconnected Island Systems

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 657 ◽  
Author(s):  
Georgios Psarros ◽  
Stavros Papathanassiou
Keyword(s):  
Linear Programming ◽  
Power Systems ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Unit Commitment ◽  
Renewable Energy Sources ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Droop Characteristic

The generation management concept for non-interconnected island (NII) systems is traditionally based on simple, semi-empirical operating rules dating back to the era before the massive deployment of renewable energy sources (RES), which do not achieve maximum RES penetration, optimal dispatch of thermal units and satisfaction of system security criteria. Nowadays, more advanced unit commitment (UC) and economic-dispatch (ED) approaches based on optimization techniques are gradually introduced to safeguard system operation against severe disturbances, to prioritize RES participation and to optimize dispatch of the thermal generation fleet. The main objective of this paper is to comparatively assess the traditionally applied priority listing (PL) UC method and a more sophisticated mixed integer linear programming (MILP) UC optimization approach, dedicated to NII power systems. Additionally, to facilitate the comparison of the UC approaches and quantify their impact on systems security, a first attempt is made to relate the primary reserves capability of each unit to the maximum acceptable frequency deviation at steady state conditions after a severe disturbance and the droop characteristic of the unit’s speed governor. The fundamental differences between the two approaches are presented and discussed, while daily and annual simulations are performed and the results obtained are further analyzed.

scholarly journals A New Heuristic Method for Transportation Network and Land Use Problem

2016 ◽  
Vol 8 (3) ◽  
pp. 94 ◽  
Author(s):  
Mouhamadou A.M.T. Bald ◽  
Babacar M. Ndiaye
Keyword(s):  
Land Use ◽  
Transportation Network ◽  
Heuristic Method ◽  
Optimization Techniques ◽  
Geographical Information ◽  
Mixed Integer ◽  
Individual Agent ◽  
The Road ◽  
Agent Simulation ◽  
Network Costs

Our paper deals with the Transportation Network and Land Use (TNLU) problem.  It consists in finding, simultaneously, the best location of urban area activities, as well as of the road network design that may minimize the moving cost in the network, and the network costs. We propose a new mixed integer programming formulation of the problem, and a new heuristic method for the resolution of TNLU. Then, we give a methodology to find locations or relocations of some Dakar region amenities (home, shop, work and leisure places), that may reduce travel time or travel distance. The proposed methodology mixes multi-agent simulation with combinatorial optimization techniques; that is individual agent strategies versus global optimization using Geographical Information System. Numerical results which show the effectiveness of the method,  and simulations based on the scenario of Dakar city are given.

Sign in / Sign up

Export Citation Format

Share Document