Shunbo Lei

Robust Risk-Constrained Unit Commitment With Large-Scale Wind Generation: An Adjustable Uncertainty Set Approach

This paper addresses two vital issues which are barely discussed in the literature on robust unit commitment (RUC): 1) how much the potential operational loss could be if the realization of uncertainty is beyond the prescribed uncertainty set; 2) how large the prescribed uncertainty set should be when it is used for RUC decision making. In this regard, a robust risk-constrained unit commitment (RRUC) formulation is proposed to cope with large-scale volatile and uncertain wind generation. Differing from existing RUC formulations, the wind generation uncertainty set in RRUC is adjustable via choosing diverse levels of operational risk. By optimizing the uncertainty set, RRUC can allocate operational flexibility of power systems over spatial and temporal domains optimally, reducing operational cost in a risk-constrained manner. Moreover, since impact of wind generation realization out of the prescribed uncertainty set on operational risk is taken into account, RRUC outperforms RUC in the case of rare events. The traditional column and constraint generation (C&CG) and two algorithms based on C&CG are adopted to solve the RRUC. As the proposed algorithms are quite general, they can also apply to other RUC models to improve their computational efficiency. Simulations on a modified IEEE 118-bus system demonstrate the effectiveness and efficiency of the proposed methodology.

Mobile Emergency Generator Pre-Positioning and Real-Time Allocation for Resilient Response to Natural Disasters

Truck-mounted mobile emergency generators (MEGs) are critical flexibility resources of distribution systems (DSs) for resilient emergency response to natural disasters. However, they are currently under-utilized. For better utilization, this paper proposes dispatching MEGs as distributed generators in DSs to restore critical loads by forming multiple microgrids (MGs). As the travel time of MEGs on road networks (RNs) can greatly influence the outage duration of critical loads, a two-stage dispatch framework consisting of pre-positioning and real-time allocation is introduced, and the traffic issue is considered via the vehicle routing problem. Pre-positioning places MEGs in staging locations prior to a natural disaster, while real-time allocation sends MEGs from staging locations to restore critical loads by forming MGs in DSs after the natural disaster strikes. Specifically, with the objective of minimizing the expected outage duration of loads considering their priorities and demand sizes, pre-positioning is done via a scenario-based two-stage stochastic optimization problem, in which the first-stage pre-positioning decisions are evaluated by numbers of second-stage real-time allocation problems corresponding to considered scenarios of DS damage and RN damage/congestion. A scenario decomposition algorithm is applied to solve this problem. Illustrative cases demonstrate the effectiveness of the proposed dispatch scheme and algorithm.

Resilience Enhancement With Sequentially Proactive Operation Strategies

Extreme weather events, many of which are climate change related, are occurring with increasing frequency and intensity and causing catastrophic outages, reminding the need to enhance the resilience of power systems. This paper proposes a proactive operation strategy to enhance system resilience during an unfolding extreme event. The uncertain sequential transition of system states driven by the evolution of extreme events is modeled as a Markov process. At each decision epoch, the system topology is used to construct a Markov state. Transition probabilities are evaluated according to failure rates caused by extreme events. For each state, a recursive value function, including a current cost and a future cost, is established with operation constraints and intertemporal constraints. An optimal strategy is established by optimizing the recursive model, which is transformed into a mixed integer linear programming by using the linear scalarization method, with the probability of each state as the weight of each objective. The IEEE 30-bus system, the IEEE 118-bus system, and a realistic provincial power grid are used to validate the proposed method. The results demonstrate that the proposed proactive operation strategies can reduce the loss of load due to the development of extreme events.

Multiperiod Risk-Limiting Dispatch in Power Systems With Renewables Integration

In this paper, an improved multiperiod risk-limiting dispatch (IMRLD) is proposed as an operational method in power systems with high percentage renewables integration. The basic risk-limiting dispatch (BRLD) is chosen as an operational paradigm to address the uncertainty of renewables in this paper due to its three good features. In this paper, the BRLD is extended to the IMRLD so that it satisfies the fundamental operational requirements in the power industry. In order to solve the IMRLD problem, the convexity of the IMRLD is verified. A theorem is stated and proved to transform the IMRLD into a piece-wise linear optimization problem that can be efficiently solved. In addition, the locational marginal price of the IMRLD is derived to analyze the effect of renewables integration on the marginal operational cost. Finally, two numerical tests are conducted to validate the IMRLD.

Identification of Critical Switches for Integrating Renewable Distributed Generation by Dynamic Network Reconfiguration

With growing penetration of renewable distributed generations (DGs) in distribution systems, effective integration of DGs has become a major concern. Distribution system dynamic reconfiguration (DSDR), which relies on real-time operations of remote-controlled switches, is potentially an efficient strategy receiving inadequate attention. Moreover, in most DSDR-related publications, normally all switches are assumed remotely controllable, which is not practical. Here we borrow the concept of critical switches to denote the switches that are most effective in accommodating DGs by DSDR. In this regard, the problem of identifying critical switches is not well investigated, although in several related publications, selected switches are assumed remote-controlled based on experience. In this work, we study the application of DSDR for DG integration. Critical switches, which optimally enable intra-day DSDR to minimize DG curtailments, are identified by limiting the number of switches to be operated and the switch-type-dependent operation constraints. Considering uncertainties of loads and DGs, the problem is formulated as a two-stage robust optimization model solved by a nested column-and-constraint generation algorithm. Illustrative cases show that DG curtailments can be significantly reduced by a small number of critical switches that operate only several times in intra-day DSDR. The proposed method can be used to provide insights for switch allocation, maintenance, and operation.

Remote-Controlled Switch Allocation Enabling Prompt Restoration of Distribution Systems

Remote-controlled switches (RCSs) play an important role in prompt service restoration of distribution systems (DSs). However, the cost of RCSs and the vast footprint of DSs limit widespread utilization of RCSs. In this paper, we present a new approach to RCS allocation for improving the performance of restoration and optimizing reliability benefits with reasonable RCS cost. Specifically, the optimal number and locations of to-be-upgraded switches can be determined with different objectives: maximizing the reduction of customer interruption cost; maximizing the reduction of system average interruption duration index; or maximizing the amount of loads that can be restored using the upgraded RCSs. We show that these models can actually be formulated as mixed-integer convex programming problems. We further introduce a novel method to equivalently transform and efficiently solve each of them. The global optimum can thus be computed within a reasonable amount of time. The IEEE 33-node and 123-node test systems are used to demonstrate the proposed models and algorithms.

Considering geographical distribution of pollutants emission in production costing

With the haze weather happening more frequently especially in developing countries, the air quality problem is attracting increasing attention. As a major source of air pollutants emission, the electricity industry is expected to conduct environmental-friendly power system planning and operation. Traditionally, the total pollutants emission is considered. However, it is well identified that the geographical distribution of pollutants emission is a significant factor determining the influence of pollutants. In this work, geographical location of power plants and loads, and meteorological conditions are considered to obtain the geographical distribution information of pollutants emission. A prototype production costing model with air quality constraints is built, which can be employed to aid generation expansion planning, fuel budgeting and system operation. Case studies are conducted on a system with both traditional generations and renewable generations, to reveal the cost of geographical consideration of pollutants.

Robust generation dispatch with wind power considering air pollutant dispersion

With increasing concerns on environmental issues, vast renewable energy is integrated into power systems worldwide, especially wind power. However, its essential uncertainty introduces great challenges into power system operation. Additionally, the emission-constrained generation dispatch problem need to be further studied in the background of restructured power systems, as just the total emission is considered traditionally. In this paper, the emission-constrained generation dispatch problem of systems including wind power is studied. Emission constraints are modeled as detailed air pollutants concentration limits in geographical positions of loads. The uncertainty of wind speed and the corresponding uncertain wind power are considered. Robust optimization is applied to cope with the uncertain information to generate dispatch plans acceptable under all possible scenarios. An algorithm based on Benders decomposition is developed to solve the proposed model. The modified IEEE 14-bus system is employed to demonstrate the effectiveness of the proposed model and algorithm.

Quantification of Intra-hour Security-Constrained Flexibility Region

Rapid growth of renewable energy sources (RES) in the generation capacity mix poses substantial challenges on the operation of power systems in various time scales. Particularly in the intra-hour time scale, the interplay among variability and uncertainty of RES, unexpected transmission/generation outages, and short dispatch lead time cause difficulties in generation-load balancing. This paper proposes a method to quantify the intra-hour flexibility region. A robust security-constrained multiperiod optimal power flow model is first constructed to quantify the frequency, magnitude, and intensity of insufficient flexibility. The randomness of RES is captured by uncertainty sets in this model. The N-k contingency, spinning reserve, and corrective control limit constraints are included. This model is then cast into a two-stage robust optimization model and solved by the column-and-constraint generation method. The emergency measures with a least number of affected buses are derived and subsequently assessed by the postoptimization sensitivity analysis. Finally, the operational flexibility region is determined by continuous perturbation on the RES penetration level and the forecast error. The IEEE 14-bus system and a realistic Chinese 157-bus system are used to demonstrate the proposed method.

Risk assessment of critical time to renewable operation with steady-state security region

Uncertain and variable characteristics of renewable energy resources introduce challenges to power system operation. A normal operating point might be drifted towards an unreliable operating point due to stochastic outputs od renewables. This paper proposes a novel method for estimating critical time to unreliable operating point with steady-state constraints. In this work, a stochastic differential equation is employed to describe the distribution of renewables with predictable tendency and stochastic errors of prediction; meanwhile, the DC power flow based steady-state security region is used to restrict the injected space. To find the critical time that uncontrollable renewables leave the security region, according the flexibility requirements defined by NERC, the uncontrollable region is identified with the Fourier-Motzkin elimination first. And then, by solving the Chebychev center problem, the critical distance for variable renewable outputs is obtained. Finally, an analytical solution of expected exit-time for renewable outputs leaving the security region is given with the Martingale stopping theorem. The proposed method can be used to construct the condition-driven risk indicators. An illustrative example is employed to demonstrate and validate the proposed method.