A review of machine learning applications in IoT-integrated modern power systems

This paper aims to provide a systematic and comprehensive survey of state-of-the-art machine learning techniques and their potential applications in IoT-integrated power systems.     

Machine-Learning based methods in short-term load forecasting

Short-term load forecasting is of great significance to the secure and efficient operation of power systems. However, loads can be affected by a variety of external impact factors and thus involve high levels of uncertainties. So it is a challenging task to achieve an accurate load forecast. This paper discusses three commonly-used machine-learning methods used for load forecasting, i.e., the support vector machine method, the random forest regression method, and the long short-term memory neural network method. The features and applications of these methods are analyzed and compared. By integrating the advantages of these methods, a fusion forecasting approach and a data preprocessing technique are proposed for improving the forecasting accuracy. A comparative study based on real load data is performed to verify that the proposed approach is capable of achieving a relatively higher forecasting accuracy.     

Impact of Phase Shifters on Locational Prices

With the introduction of restructuring in the electric power industry, the price of electricity has become the focus of power market activities. Phase shifters in a power system can mitigate or reduce transmission congestion by redirecting line flows, reduce the cost of power dispatch by adjusting locational marginal prices (LMPs), and enhance market competition by reducing the chance of market power occurrences due to limited transmission flows. This paper analyzes the role of phase shifters in restructured power systems by simulating electricity market prices. The paper further provides a comparison among various alternatives such as transmission expansion for mitigating congestion. The simulation results are analyzed for a three-bus and presented for the Institute of Electrical and Electronics Engineers (IEEE) 118-bus power system.     

GENCO's Risk-Based Maintenance Outage Scheduling

This paper presents a stochastic model for the optimal risk-based generation maintenance outage scheduling based on hourly price-based unit commitment in a generation company (GENCO). Such maintenance outage schedules will be submitted by GENCOs to the ISO for approval before implementation. The objective of a GENCO is to consider financial risks when scheduling its midterm maintenance outages. The GENCO also coordinates its proposed outage scheduling with short-term unit commitment for maximizing payoffs. The proposed model is a stochastic mixed integer linear program in which random hourly prices of energy, ancillary services, and fuel are modeled as scenarios in the Monte Carlo method. Financial risks associated with price uncertainty are considered by applying expected downside risks which are incorporated explicitly as constraints. This paper shows that GENCOs could decrease financial risks by adjusting expected payoffs. Illustrative examples show the calculation of GENCO's midterm generation maintenance schedule, risk level, hourly unit commitment, and hourly dispatch for bidding into energy and ancillary services markets.     

Reliability evaluation of a three-area power system

This paper evaluates the reliability of a three-area power system. It considers a new algorithm to determine the amount of MW assistance available to a deficient area in a power pool. The algorithm considers the area of interest and its neighboring areas in terms of their two-area reliability and then extends the results to the multi-area system. The linear flow model is used to calculate the energy supply to the importing area in the power pool, where the tie lines may impose limits on import-export capabilities.

This approach does not set any restrictions as to how the areas may be interconnected.     

Deployment of cybersecurity for managing traffic efficiency and safety in smart cities

Boosting the concept of smart cities for implementing an intelligent management of traffic congestion while reducing cybersecurity concerns will not only be more efficient for reducing traffic congestion but also more resilient to cyber incidents. In this paper we proposed a framework that can act as a generalized firewall and work interactively with several critical infrastructures in a smart city to protect the respective operations from a variety of cyber threats. The objective is to develop several steps for a comprehensive traffic management framework in smart cities that facilitates the cooperation among drivers and between drivers and the traffic management authority. The transformative nature of the proposed study supports its applications to a variety of networked critical infrastructures, including electricity, gas, water, rails, and telecommunications, as they intend to respond effectively to a wide range of weather- or human-related disruptions. The contributions of this paper include: Improving the traffic management performance in urban transportation systems, assessing and mitigating the cybersecurity risk in urban traffic management, and facilitating efficient and cyber-secure traffic management in metropolitan areas; Developing and testing an interactive simulation platform for evaluating the traffic management performance under various traffic conditions; Validating and demonstrating the applications in a practical urban transportation system; Disseminating the proposed study results to a wide range of concerned audiences via user-group meetings, detailed education forums, and a close collaboration with the local traffic management authority.     

A fuzzy-based optimal reactive power control

A mathematical formulation of the optimal reactive power control problem using fuzzy set theory is presented. The objectives are to minimize real power losses and improve the voltage profile of a given system. Transmission losses are expressed in terms of voltage increments by relating the control variables to the voltage increments in a modified Jacobian matrix. This formulation does not require Jacobian matrix inversion, and hence it will save computation time and memory space. The objective function and the constraints are modeled by fuzzy sets. Linear membership functions of the fuzzy sets are defined and the fuzzy linear optimization problem is formulated. The solution space is defined as the intersection of the fuzzy sets describing the constraints and the objective functions. Each solution is characterized by a parameter that determines the degree of satisfaction with the solution. The optimal solution is the one with the maximum value for the satisfaction parameter. Results for test systems reveal the advantages of the approach     

Impact of Security on Power Systems Operation

This paper reviews the status of security analyses in vertically integrated utilities and discusses the impact of system security on the operation and the planning of restructured power systems. The paper is focused on the static security rather than the dynamic security of power systems. The paper also discusses assumptions, functions, and calculation tools that are considered for satisfying power systems security requirements. In addition, the security coordination among time-based scheduling models is presented. In particular, real-time security analysis, short-term operation,midterm operation planning, and long-term planning are analyzed. The paper highlights issues and challenges for implementing security options in electricity markets and concludes that global analyses of security options could provide additional opportunities for seeking optimal and feasible schedules in various time scales.     

Random perturbation of transfer capability and its effect on power system reliability evaluation

In this paper the mean first passage time of a dynamic system from its domain of attraction has been considered as a reliability index. The effect of environmental fluctuations and the operational characteristics of the transmission lines have been regarded as the input noise to the system with multistable states. Several design criteria have been examined in order to determine the effect of system configuration on power system reliability evaluation. The proposed technique does not set any restrictions on the system topology and can be implemented by system planners as an aid to identify the transmission system bottlenecks and compare different options in the planning of a large-scale power system.     

Effects of ramp-rate limits on unit commitment and economicdispatch

The authors propose an algorithm to consider the ramp characteristics in starting up and shutting down the generating units as well as increasing and decreasing power generation. They consider the inclusion of ramping constraints in both unit commitment and economic dispatch. Since implementing ramp-rate constraints is a dynamic process, dynamic programming (DP) is a proper tool to treat this problem. To overcome the computational expense which is the main drawback of DP, this study initially employs artificial intelligence techniques to produce a unit commitment schedule which satisfies all system and unit operation constraints except unit ramp-rate limits. Then, a dynamic procedure is used to consider the ramp properties as units are started up and shut down. According to this adjustment, maximum generating capabilities of units will change the unit operation status instead of following a step function. Finally, a dynamic dispatch procedure is adopted to obtain a suitable power allocation which incorporates the unit generating capability information given by unit commitment and unit ramping constraints, as well as the economical considerations. Two examples are presented to demonstrate the efficiency of the method