Biblio

In modern grid systems which is a typical cyber-physical System (CPS), information space and physical space are closely related. Once the communication link is interrupted, it will make a great damage to the power system. If the service path is too concentrated, the risk will be greatly increased. In order to solve this problem, this paper constructs a route planning algorithm that combines node load pressure, link load balance and service delay risk. At present, the existing intelligent algorithms are easy to fall into the local optimal value, so we chooses the deep reinforcement learning algorithm (DRL). Firstly, we build a risk assessment model. The node risk assessment index is established by using the node load pressure, and then the link risk assessment index is established by using the average service communication delay and link balance degree. The route planning problem is then solved by a route planning algorithm based on DRL. Finally, experiments are carried out in a simulation scenario of a power grid system. The results show that our method can find a lower risk path than the original Dijkstra algorithm and the Constraint-Dijkstra algorithm.

This paper studies the modeling of cyber-physical dependencies observed within power grids and the effects of these intra-dependencies, on power grid resilience, which is evaluated quantitatively. A fundamental contribution of this paper is the description of the critically important role played by cyber-physical buffers as key components to limit the negative effect of intra-dependencies on power grids resilience. Although resilience issues in the electric power provision service could be limited thanks to the use of local energy storage devices as the realization of service buffers, minimal to no autonomy in data connectivity buffers make cyber vulnerabilities specially critical in terms of resilience. This paper also explains how these models can be used for improved power grids resilience planning considering internal cyber-physical interactions.

The penalization of the objective function due to energy deficits is a key element for determining the operational policy of hydroelectric reservoirs. Its definition impacts not only operations, but also system expansion. Brazil historically defined these penalties with basis on a proxy of the economic deficit cost, a value in \$/MWh obtained with aid of the Input-Output Matrix. We propose an approach where these penalties are obtained in order to minimize the operation cost and cost of rationing of the system, considering a criterion of security of supply. A case study with data from the Brazilian System illustrates its application.

In order to meet the demand of electrical energy by consumers, utilities have to maintain the security of the system. This paper presents a design of the Microgrid Central Energy Management System (MCEMS). It will plan operation of the system one-day advance. The MCEMS will adjust itself during operation if a fault occurs anywhere in the generation system. The proposed approach uses Dynamic Programming (DP) algorithm solves the Unit Commitment (UC) problem and at the same time enhances the security of power system. A case study is performed with ten subsystems. The DP is used to manage the operation of the subsystems and determines the UC on the situation demands. Faults are applied to the system and the DP corrects the UC problem with appropriate power sources to maintain reliability supply. The MATLAB software has been used to simulate the operation of the system.

In this study, it is proposed to carry out an efficient formulation in order to figure out the stochastic security-constrained generation capacity expansion planning (SC-GCEP) problem. The main idea is related to directly compute the line outage distribution factors (LODF) which could be applied to model the N - m post-contingency analysis. In addition, the post-contingency power flows are modeled based on the LODF and the partial transmission distribution factors (PTDF). The post-contingency constraints have been reformulated using linear distribution factors (PTDF and LODF) so that both the pre- and post-contingency constraints are modeled simultaneously in the SC-GCEP problem using these factors. In the stochastic formulation, the load uncertainty is incorporated employing a two-stage multi-period framework, and a K - means clustering technique is implemented to decrease the number of load scenarios. The main advantage of this methodology is the feasibility to quickly compute the post-contingency factors especially with multiple-line outages (N - m). This concept would improve the security-constraint analysis modeling quickly the outage of m transmission lines in the stochastic SC-GCEP problem. It is carried out several experiments using two electrical power systems in order to validate the performance of the proposed formulation.

This study proposes to apply an efficient formulation to solve the stochastic security-constrained generation capacity expansion planning (GCEP) problem using an improved method to directly compute the generalized generation distribution factors (GGDF) and the line outage distribution factors (LODF) in order to model the pre- and the post-contingency constraints based on the only application of the partial transmission distribution factors (PTDF). The classical DC-based formulation has been reformulated in order to include the security criteria solving both pre- and post-contingency constraints simultaneously. The methodology also takes into account the load uncertainty in the optimization problem using a two-stage multi-period model, and a clustering technique is used as well to reduce load scenarios (stochastic problem). The main advantage of this methodology is the feasibility to quickly compute the LODF especially with multiple-line outages (N-m). This idea could speed up contingency analyses and improve significantly the security-constrained analyses applied to GCEP problems. It is worth to mentioning that this approach is carried out without sacrificing optimality.