Yaser Tohidi

Profit-maximization generation maintenance scheduling through bi-level programming

This paper addresses the generation maintenance scheduling (GMS) dilemma in a deregulated power system. At first, under a centralized cost minimization framework, a GMS is formulated and set as the benchmark (cost-minimization GMS). Then, the cost-minimization is changed into a profit-maximization problem of generation companies (GENCOs) and the GMS is developed as a bi-level optimization. Karush–Kuhn–Tucker conditions are applied to transform the bi-level into a single-level mixed-integer linear problem and subsequently, Nash equilibrium is obtained as the final solution for the GMS under a deregulated market (profit-maximization GMS). Moreover, to incorporate reliability and economic regulatory constraints, two rescheduling signals (incentive and penalty) are considered as coordination processes among GENCOs and independent system operators. These signals are based on energy-not-supplied and operation cost, and ensure that the result of profit-maximization GMS is in the given reliability and social cost limits, respectively. These limits are obtained from the cost-minimization GMS. Lastly, the model is evaluated on a test system. The results demonstrate applicability and challenges in GMS problems.

Sequential Coordination of Transmission Expansion Planning With Strategic Generation Investments

This paper proposes mathematical models for sequential coordination of transmission expansion planning with strategic generation investments. The proactive and reactive coordinations are modeled and studied. The interaction between transmission company (Transco) and strategic generation companies (Gencos) is modeled using the sequential-move game. This is while the interaction between the strategic Gencos is modeled as a simultaneous-move game. In the proactive coordination, the Transco expands its future transmission capacities taking into account the strategic investments by Gencos. In the reactive coordination, strategic Gencos move first and expand their future generation capacities and then Transco expands the transmission capacity. The proactive coordination is modeled as a mixed-integer bilevel linear program (MIBLP) and the reactive coordination is modeled as a mixed-integer linear program (MILP). The MIBLP has binary variables in both upper and lower levels. The Moore–Bard algorithm is parallelized and used to solve the MIBLP. The mathematical models and the parallelized Moore–Bard algorithm are tested on 3-bus and 6-bus example systems and the modified IEEE-RTS96. Also, the IEEE 118-bus test system is studied using a heuristic version of the Moore–Bard algorithm.

Coordination of Generation and Transmission Development Through Generation Transmission Charges—A Game Theoretical Approach

Transmission charges aim to recover the cost of transmission network investments and provide efficient locational signals to new generators. In this paper, we investigate the effect of these charges on the development of new generation capacities in the system. Generation expansion planning is decided by strategic generation planners (SGPs) trying to maximize their profits, while transmission line investments are planned by a central planner and regulatory body aimed at minimizing the overall operation and network investment costs of the system. Regulatory transmission charges (RTCs) are calculated according to the marginal responsibility of generation investment on transmission network investment costs. An iterative algorithm is proposed to model the interaction taking place between the central planner and SGPs. The developed methodology is applied to a 2-node illustrative example and the IEEE-RTS96, and effects of RTCs on investment decisions of SGPs are analyzed.

A mathematical model for strategic generation expansion planning

This paper proposes a mathematical model for strategic generation expansion planning problem. The model is developed based on the the simultaneous-move game between Gencos. Gencos investment decisions are passed to the dispatch center which decides about the production level in operating scenarios considered. Using Karush-Kuhn-Tucker conditions (KKTs) and disjunctive linearization, the model is formulated as a mixed-integer linear program (MILP). The concepts of worst Nash equilibrium (WNE) and best Nash equilibrium (BNE) are introduced to handle multiple NE problem. The impact of uncertainty (scenarios) on equilibria band, i.e., the difference between WNE and BNE is discussed. The developed model is simulated on illustrative 2-node and 3-node example systems and also on IEEE-RTS96 test system.

Reactive coordination of transmission-generation investment planning

This paper discusses the reactive coordination of generation and transmission planning in a competitive electricity market. The transmission planner is assumed as a social cost minimizing entity and the behavior of generators is modeled as the Nash equilibrium of a strategic game. Reactive coordination forms a mixed-integer optimisation problem which can be solve by commercially available softwares. This problem is solved for two cases of with and without transmission charges. Efficient coordination of transmission and generation planning is considered as the benchmark in this study. The developed model is implemented for a 6-node illustrative example and IEEE-RTS96 test system and the results are compared.

Volt/VAR Optimization function with load uncertainty for planning of MV distribution networks

Volt/VAR Optimization (VVO) function is an important element in real time operation of distribution networks and major part of advanced Distribution Management Systems (DMS). From planning prospective, VVO function can be used to optimize reactive power flow in distribution network to recommend the best operating condition for the control equipment in a predefined period of time in future (i.e. 24 hour). The typical objective function of VVO functions are minimizing the total system loss for a certain system load level. VVO function computes the optimized setting for transformer on-load tap changers (OLTC), Voltage Regulators (VR), and Capacitor Banks, while system voltage profile is maintained within its limits. In this paper the objective is to develop a planning VVO engine which can calculate the most probable expected loss of the network for the next 24 hours, and can recommend the best expected operating condition for the control equipment. For the VVO algorithm a full mixed integer linear programming (MILP) model is used to solve the loss objective of VVO problem for a planning application. The load uncertainty is modeled by an ARMA model which can create any arbitrary number of forecasted load scenarios to be used by VVO engine (implemented in a commercial solver GAMS, “General Algebraic Modeling System”). The implemented models have been tested on a real distribution network in southern Sweden and the results are presented.

An LFB-based algorithm for fast recovery of a power system from contingencies

Power systems are becoming older and more intense. Thus, disturbances are inevitable and it is important to develop a powerful methodology to lessen the damages afterward. This paper proposes a new linear formulation based on an approximated version of AC power flow model to compromise between speed and accuracy of decision making process. In the proposed method, voltage and reactive power violations as well as transmission system overloads are alleviated by either or both of generation rescheduling and load shedding. The developed method adopts the line flows and square of voltage magnitudes as the problem state variables. Simulation results on the IEEE-RTS96 system reveal that the proposed method outperforms both the conventional and heuristic approaches from the execution time and the computational effort viewpoints while keeping the accuracy of the results.

Multi-regional transmission planning under interdependent wind uncertainty

Planning and operation of power systems with large amounts of wind power is a challenging topic in nowadays power system researches. These studies assess the impact of wind powers variability and uncertainty on power system. Wind power uncertainty has some characteristics such as interdependent uncertainty between adjacent regions. This paper evaluates the effect of wind powers uncertainty and its interdependent characteristic between adjacent regions on cross-border lines investment in multi-region transmission systems. Transmission planners are the responsible bodies in each region and maximise their own region social welfare considering the transmission planning decisions of other transmission planners. In order to accomplish this task, the model developed in [1] for non-cooperative transmission planning and uncertainty modeling is used with some modifications according to the objective of this paper. The approach is simulated on a 9-bus network consisting of three inter-connected transmission systems for two cases of (1) independent uncertainty between wind resources and (2) complete interdependent uncertainty between wind resources.

Free riding effect in multi-national transmission expansion planning

The transmission system in EU is run by different TSOs with different nationalities, ownerships, and internal policies. This makes the transmission planning for the European Integrated Electricity Market difficult. In fact, each TSO tries to act strategically in order to maximise his social welfare and they are reluctant to act in a cooperative solution. In the context of multiple inter-connected transmission systems, its very important to incentives TSOs to act in a cooperative solution. However, free riders are always waiting for other TSOs to push on transmission expansion and they make benefit out of it without contributing to the expansion. In this paper, we model the free riding effect and analyse its economic consequences in an inter-connected network by comparing two cases: considering congestion revenue just for cross-border lines and considering congestion revenue not only for cross-border lines but also for intra-area lines. Each transmission planner is modelled as a social-welfare maximising firm using a mixed-integer linear programming problem. This study is carried out on a Nine-bus network consisting of three TSOs.