• Energy Research

An integrated long-term scheduler (LTS) for a GENCO with local transmission lines and numerous constraints is proposed. The proposed algorithm extends the Benders decomposition to include network, fuel and emission constraints into LTS. The local network is modeled as a probabilistic problem to include the effect of generation and transmission outages. The proposed algorithm will be used by GENCO planners and ISO for ATC calculations.

To address microgrid tie flow errors caused by wind generation variability, this paper proposes and develops a multi-time scale coordinated control and scheduling strategy for inverter-based thermostatically controlled loads (TCLs). First, in hour-time scale, inverter-based TCLs with adjusting temperature set-point are modeled as virtual generators to compensate tie flow deviations in the day-ahead plan. Next, in minute-time scale, virtual batteries representing operating behaviors of inverter -based TCLs with frequency control are scheduled determined by the control of virtual generators in hour-time scale. The virtual batteries are scheduled to smooth out tie flow errors corresponding to day-ahead plan and hour-time scale schedules. The multi-time scale control methods are coordinated to employ the response potential of inverter-based TCLs and response curve-based methods are proposed to control inverter-based TCLs considering the customer privacy. The multi-time scale stochastic schedules which are based on response curves of inverter-based TCLs are coordinated to accommodate wind generation variability. Simulation results demonstrate that the microgrid tie flow errors are effectively mitigated by the proposed multi-time scale coordinated control and scheduling of inverter -based TCLs.

This paper describes a scheduling method for representing the thermal stress of turbine shafts as ramp rate constraints in the thermal commitment and dispatch of generating units. The paper uses Lagrangian relaxation for optimal generation scheduling. In applying the unit commitment, thermal stress over the elastic limit is used for calculating the ramping cost. The thermal stress contribution to generation cost requires the calculation of a set that includes thermal stress at the end of each time step; this requirement presents a complicated problem which cannot be solved by an ordinary optimization method such as dynamic programming. The paper uses an improved simulated annealing method to determine the optimal trajectory of each generating unit. Furthermore, the paper uses linear programming for economic dispatch in which thermal stress limits are incorporated in place of fixed ramp rate limits. The paper illustrates the economics of frequently ramping up/down of low cost generating units versus the cost of replacement of their turbine rotors with a shorter life span. The experimental results for a practical system demonstrate the effectiveness of the proposed method in optimizing the power system generation scheduling.

An operation model for distribution companies (DISCOs) is proposed to reduce their operation costs by fully utilizing the flexibility of electric vehicle aggregators (EVAs). In the proposed model, linear decision rules approximation is adopted to achieve mathematical tractability, and distributionally robust optimization is applied to evaluate costs affected by uncertainties in renewable power outputs and EVA charging demands. Case studies are conducted under various settings. With the proposed model, using EVAs to mitigate uncertainties is achieved and is further classified into delaying uncertainties and eliminating uncertainties. As a result, average penalties for DISCO’s deviations from its planned energy portfolio are reduced. Besides, EVA charging demands are shifted to hours with lower energy prices to reduce energy costs of DISCO. Using EVAs to mitigate uncertainties and shifting EVA charging demands are properly coordinated under the proposed model. Moreover, power losses in EVA charging and discharging are utilized to reduce the scale of uncertainties, which decreases average penalties for energy deviations of DISCO.

In this letter, we propose a data-driven warm start approach, empowered by artificial neural networks, to boost the efficiency of convex relaxations in optimal gas flow. Case studies show that this approach significantly decreases the number of iterations for the convex-concave procedure algorithm, and optimality and feasibility of the solution can still be guaranteed. 3 pages, 4 tables, submitted to IEEE PES Letter

This paper proposes an integrated framework based on covariates, which coordinates short-term generation and transmission maintenance scheduling with midterm maintenance decisions by considering the effects of short-term security-constrained unit commitment (SCUC). A recursive sampling method is introduced in the proposed Monte Carlo-based framework for generating scenarios, in which the effects of component aging and covariates on the outage process are quantified by the proportional hazard model (PHM). For each sampled scenario, an iterative dynamic scenario updating approach is introduced to consider interactions among covariate conditions, random component outages, and maintenance outage scheduling. The co-optimization problem is decoupled into three separate optimization subproblems by Lagrangian relaxation (LR), which include generation maintenance scheduling, transmission maintenance scheduling, and short-term SCUC problems. Each scenario is dynamically updated based on the optimal maintenance outage and SCUC solutions, and maintenance and SCUC solutions are re-optimized using the updated scenario. The iterative procedure stops when neither the optimal schedule nor the dynamic scenario changes any further. The overall convergence of the proposed Monte Carlo-based framework is checked by the coefficient of variation (CV) of costs over multiple scenarios. Case studies on the 6-bus system and the IEEE 118-bus system are used to exhibit the effectiveness of proposed framework.

This paper presents a new method for minimizing transmission line losses and improving voltage profile in a given system by adjusting control variables, i.e., tap position of transformers and reactive power injection of VAR sources. Transmission losses are considered as a function of voltage increments. The control variables and voltage increments are related by a modified Jacobian matrix. Linear Programming (LP) is used to calculate the voltage increments which minimize the transmission losses, and the adjustments of control variables are obtained by a modified Jacobian matrix. Since this method does not need any matrix inversion, it will save computational time and memory space.