A Multiperiod OPF Model Under Renewable Generation Uncertainty and Demand Side Flexibility

Renewable energy sources such as wind and solar have received much attention in recent years and large amount of renewable generation is being integrated to the electricity networks. A fundamental challenge in power system operation is to handle the intermittent nature of the renewable generation. In this paper we present a stochastic programming approach to solve a multiperiod optimal power flow problem under renewable generation uncertainty. The proposed approach consists of two stages. In the first stage operating points for conventional power plants are determined. Second stage realizes the generation from renewable resources and optimally accommodates it by relying on demand-side flexibility. The benefits from its application are demonstrated and discussed on a 4-bus and a 39-bus systems. Numerical results show that with limited flexibility on the demand-side substantial benefits in terms of potential additional re-dispatch costs can be achieved. The scaling properties of the approach are finally analysed based on standard IEEE test cases upto 300 buses, allowing to underlined its computational efficiency.Comment: 8 pages, 10 figure

Co-optimization of energy and reserve capacity considering renewable energy unit with uncertainty

This paper proposes a system model for optimal dispatch of the energy and reserve capacity considering uncertain load demand and unsteady power generation. This implicates uncertainty in managing the power demand along with the consideration of utility, user and environmental objectives. The model takes into consideration a day-ahead electricity market that involves the varying power demand bids and generates a required amount of energy in addition with reserve capacity. The lost opportunity cost is also considered and incorporated within the context of expected load not served. Then, the effects of combined and separate dispatching the energy and reserve are investigated. The nonlinear cost curves have been addressed by optimizing the objective function using robust optimization technique. Finally, various cases in accordance with underlying parameters have been considered in order to conduct and evaluate numerical results. Simulation results show the effectiveness of proposed scheduling model in terms of reduced cost and system stability

Dynamic Energy Management

We present a unified method, based on convex optimization, for managing the power produced and consumed by a network of devices over time. We start with the simple setting of optimizing power flows in a static network, and then proceed to the case of optimizing dynamic power flows, i.e., power flows that change with time over a horizon. We leverage this to develop a real-time control strategy, model predictive control, which at each time step solves a dynamic power flow optimization problem, using forecasts of future quantities such as demands, capacities, or prices, to choose the current power flow values. Finally, we consider a useful extension of model predictive control that explicitly accounts for uncertainty in the forecasts. We mirror our framework with an object-oriented software implementation, an open-source Python library for planning and controlling power flows at any scale. We demonstrate our method with various examples. Appendices give more detail about the package, and describe some basic but very effective methods for constructing forecasts from historical data.Comment: 63 pages, 15 figures, accompanying open source librar

Robust Optimization for SCED in AC-HVDC Power Systems

Wind power is a clean, renewable and low-carbon resource for power generation that has received increasing attention in power systems over the last few decades. There are two main challenges associated with the large-scale integration of wind power plants in the power system: i) the intermittent nature of wind power results in prediction errors that can greatly impact the system's operational security and reliability requirements, and ii) large-scale offshore wind farms are typically located far from onshore loads and require new developments in the transmission system of power grids, e.g., realization of mixed alternating current-high voltage direct current (AC-HVDC) power systems, which will introduce new reliability requirements to the system operator. The security-constrained economic dispatch (SCED) problem deals with determining a power dispatch schedule, for all generating units, that minimizes the total operational cost, while taking into account system reliability requirements. Robust optimization (RO) has recently been used to tackle wind power uncertainty in the SCED problem. In the literature of RO, the budget of uncertainty was proposed to adjust the solution conservatism (robustness) such that higher budgets of uncertainty correspond to more conservative solutions. This thesis shows that the budget of uncertainty approach may not be meaningful for problems with RHS uncertainty since increasing the budget of uncertainty by more than a certain threshold may not always impact the level of conservatism. This thesis proposes a new tractable two-stage robust optimization model that effectively incorporates the budget of uncertainty in problems with RHS uncertainty, controls the level of conservatism, and provides meaningful insights on the trade-off between robustness and cost. Furthermore, this thesis examines the applicability of the proposed robust approach for the SCED problem in mixed AC-HVDC power systems with large integration of wind power. The proposed robust SCED model considers the impact of wind power curtailment on the operational cost and reliability requirements of the system. Extensive numerical studies are provided to demonstrate the economic and operational advantages of the proposed robust SCED model in mixed AC-HVDC systems from five aspects: the effectiveness of the budget of uncertainty, robustness against uncertainty, contribution to real-time reliability, cost efficiency, and power transfer controllability

Prescriptive Analytics in Electricity Markets

Electricity markets are a clear example of a sector in which decision making plays a crucial role in its daily activity. Moreover, uncertainty is intrinsic to electricity markets and affects most of the tasks that agents operating in them must carry out. Many of these tasks involve decisions characterized by low risk and being addressed periodically. In this thesis, we refer to these tasks as iterative decisions. This thesis applies the aforementioned innovative frameworks for decision making under uncertainty using contextual information in iterative decision making tasks faced daily by electricity market agents.Decision making is critical for any business to survive in a market environment. Examples of decision making tasks are inventory management, resource allocation or portfolio selection. Optimization, understood as the scientific discipline that studies how to solve mathematical programming problems, can help make more efficient decisions in many of these situations. Particularly relevant, because of their frequency and difficulty, are those decisions affected by uncertainty, i.e., in which some of the parameters that precisely determine the optimization problem are unknown when the decision must be made. Fortunately, the development of information technologies has led to an explosion in the availability of data that can be used to assist decisions affected by uncertainty. However, most of the available historical data do not correspond to the unknown parameter of the problem but originate from other related sources. This subset of data, potentially valuable for obtaining better decisions, is called contextual information. This thesis is framed within a new scientific effort that seeks to exploit the potential of data and, in particular, of contextual information in decision making. To this end, in this thesis, we have developed mathematical frameworks and data-driven optimization models that exploit contextual information to make better decisions in problems characterized by the presence of uncertain parameters

Co-Optimization of Gas-Electricity Integrated Energy Systems Under Uncertainties

In the United States, natural gas-fired generators have gained increasing popularity in recent years due to low fuel cost and emission, as well as the needed large gas reserves. Consequently, it is worthwhile to consider the high interdependency between the gas and electricity networks. In this dissertation, several co-optimization models for the optimal operation and planning of gas-electricity integrated energy systems (IES) are proposed and investigated considering uncertainties from wind power and load demands. For the coordinated operation of gas-electricity IES: 1) an interval optimization based coordinated operating strategy for the gas-electricity IES is proposed to improve the overall system energy efficiency and optimize the energy flow. The gas and electricity infrastructures are modeled in detail and their operation constraints are fully considered. Then, a demand response program is incorporated into the optimization model, and its effects on the IES operation are investigated. Interval optimization is applied to address wind power uncertainty in IES. 2) a stochastic optimal operating strategy for gas-electricity IES is proposed considering N-1 contingencies in both gas and electricity networks. Since gas pipeline contingencies limit the fuel deliverability to gas-fired units, N-1 contingencies in both gas and electricity networks are considered to ensure that the system operation is able to sustain any possible power transmission or gas pipeline failure. Moreover, wind power uncertainty is addressed by stochastic programming. 3) a robust scheduling model is proposed for gas-electricity IES with uncertain wind power considering both gas and electricity N-1 contingencies. The proposed method is robust against wind power uncertainty to ensure that the system can sustain possible N-1 contingency event of gas pipeline or power transmission. Case studies demonstrate the effectiveness of the proposed models. For the co-optimization planning of gas-electricity IES: a two-stage robust optimization model is proposed for expansion co-planning of gas-electricity IES. The proposed model is solved by the column and constraint generation (C&CG) algorithm. The locations and capacities of new gas-fired generators, power transmission lines, and gas pipelines are optimally determined, which is robust against the uncertainties from electric and gas load growth as well as wind power