• Energy Research

Fossil fuel thermal power plants constitute a large part of the Turkish electricity generation capacity. Turkish government has been developing several energy policy documents to evaluate how various renewable energy sources of the country can be utilized optimally in the generation of electricity within the next 30 years. This study considers three scenarios in the transition to renewable energy for Turkey; the business as usual (BAU), energy conservation (EC) and renewable energy (REN) scenarios. EC scenario considers the use of energy-efficient appliances and imposing a carbon tax, whereas REN scenario considers increasing the share of the renewable energy sources as much as possible in the power generation mix. These scenarios were evaluated in terms of cost and environmental impact. The LEAP (Long-range Energy Alternatives Planning Model) was used in the research. The REN scenario has been shown to be the optimal energy policy option for Turkey in terms of cost and environmental impact.

This paper introduces integrated models for transmission investments anticipating the generation investments and market-clearing equilibrium. Market-clearing models for deregulated electricity markets can inform decision makers on price signals formed in the competitive market, other investor's and/or generator's behaviors underlying these price signals and new generation/transmission investment decisions. Bi-level programming problems are formed for this integrated models and reformulated by using mathematical programs with equilibrium constraints (MPEC). A simultaneous optimization model (as mixed complementarity problem-MCP) is proposed to compute the same equilibrium solution of the MPEC problem. The proposed MCP model is found to be computationally more efficient than the traditional MPEC reformulations on a simple 3-bus example. These models will be useful in planning generation/transmission investments, and analyzing the relations among these investments and the market outcomes.

Abstract The transition to a low-carbon energy economy will remain a cornerstone of national energy policies of countries committed to the climate change accord for decades to come. We think that transmission investment is one key policy instrument amongst other to help with this transition. We propose an enhanced role for investing in transmission capacity for inter-regional trade to allow effective fuel switching among countries through a physically connected market. We develop a conceptual framework of “Regional Energy Hubs” and propose a cost minimization model in support of a transmission investment strategy that integrates: (i) a key geopolitical parameter for countries that are geographically close in a region but under different political jurisdictions, judged as stable and receptive to firm trading arrangements, (ii) an economic parameter related to the fuel mix where the differences in a country's supply and demand characteristics are significant enough for allow mutual benefits to be realized through cost reduction, (iii) an environmental parameter linked to a country's carbon intensity that could benefit from the resources of a neighboring jurisdiction with lower intensities, and (iv) a financial parameter for each country within a region capable of attracting investment capital. A connected ‘Regional Energy Hub’ offers large economic and environmental benefit for transition to a low-carbon energy economy.

In this paper, we propose variational inequality models for electricity markets with time-of-use (TOU) pricing. Demand response is dynamic in the model through a dependence on the lagged demand. Different market structures are examined within this context. With an illustrative example, the welfare gains/losses are analyzed after an implementation of TOU pricing scheme over the single pricing scheme. It is intended that the proposed models would be useful: 1) for regulatory bodies in jurisdictions to assess market power; 2) to forecast future TOU prices; and 3) to examine welfare changes in electricity markets that change to TOU pricing from single pricing.

Many electricity market models have either mostly ignored the demand response to changing prices (e.g., day-ahead models with mostly fixed demand), or, at the other extreme, they assumed that the full demand response occurred within one hour. Moreover, the capital stock adjustment and the forward-looking nature of consumers are usually omitted. In this paper, we propose variational inequality models for electricity markets with dynamic demand models where the intertemporal nature of consumption (i.e., the current consumption decision affects capital stock/habits and thus the future preferences and demand) is recognized. It is intended that the proposed models would develop a framework for electricity market equilibrium models that incorporate the dynamics of the demand side.

Suppliers in competitive electricity markets regularly respond to prices that change hour by hour or even more frequently, but most consumers respond to price changes on a very different time scale, i.e., they observe and respond to changes in price as reflected on their monthly bills. In this paper, we examine mixed complementarity programming models of equilibrium that can bridge the speed of response gap between suppliers and consumers yet adhere to the principle of marginal cost pricing of electricity. We develop a computable equilibrium model to estimate ex ante time-of-use (TOU) prices for a retail electricity market. It is intended that the proposed models would be useful 1) for jurisdictions (e.g., Ontario) where consumers' prices are regulated, but suppliers offer into a competitive market, 2) for forecasting forward prices in unregulated markets, and 3) in evaluation and welfare analysis of the policies regarding regulated TOU pricing compared to regulated single pricing

Abstract Public concern over energy supplies, prices, sustainability and efficiency has emerged as a major issue around the world. Yet most of what we know regarding public opinion on energy comes from North America and Europe. This paper presents the results from the 2016 Turkish Public Preferences for Energy Survey, which included 1204 respondents and examined Turkish residents’ household energy consumption, energy policy preferences, and environmental concerns. The main findings were that Turkish citizens consider natural gas and electricity highly expensive, view dependence on imported energy as Turkey's most pressing energy challenge, and recognize the problem of climate change. This lends public support for wind and solar power, but, at the same time, energy issues and the environment policies of political parties do not affect voting choices and political preferences.

In this paper, a modification to Dantzig-Wolfe (DW) decomposition algorithm for variational inequality (VI) problems is considered to alleviate the computational burden and to facilitate model management and maintenance. As proposals from DW subproblems are accumulated in the DW master problem, the solution time and memory requirements are increasing for the master problem. Approximation of the DW master problem solution significantly reduces the computational effort required to find the equilibrium. The approximate DW algorithm is applied to a time of use pricing model with realistic network constraints for the Ontario electricity market and to a two-region energy model for Canada. In addition to empirical analysis, theoretical results for the convergence of the approximate DW algorithm are presented. Dantzig-Wolfe decomposition of variational inequality problems is considered.Convergence theory is presented for the approximation of the master problem.Empirical results for two energy market models are presented.Advantages on computations and model management/maintenance are presented.

In this study, we consider a collusion model for competitive pool based electricity markets operated by an independent system operator (ISO), where it aims to prevent tacit collusion among generators. In order to determine the existence of tacit collusion in the market, we have employed the the game-theoretic bilevel optimization model proposed by [1]. This model represents the market clearing mechanism, where generators determine their bids in order to maximize their profit while the system operator allocates power and determine locational electricity prices. The resulting optimization problem is a bilevel multi-criteria problem with non-linear terms, which is already complex and difficult to solve. We provide reformulations and linearization methods to obtain equivalent problems, e.g., a mathematical problem with equilibrium constraints (MPEC), a mixed integer non-linear problem (MINLP) and a mixed integer problem (MIP). A simple 6-bus system is used to test the rate of detection for collusive states for the reformulations and the results are presented. We have found that MIP models and reformulations detect strong collusive states. This model can guide ISOs in identifying and preventing cases of generator collusion.