Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

Designing a Robust Decentralized Energy Transactions Framework for Active Prosumers in Peer-to-Peer Local Electricity Markets

In this paper, a fully decentralized local energy market based on peer-to-peer(P2P) trading is proposed for small-scale prosumers. In the proposed market, the prosumers are classified as buyers and sellers and can bilaterally engage in energy trading (P2P) with each other. The buyer prosumers are equipped with electrical storage and can participate in a demand response (DR) program while protecting their privacy. In addition to bilateral negotiating with the local sellers, these players can compensate for their energy deficiency from the upstream market as the retail market at hours without local generation. In this paper, the retail market price is assumed uncertain. Robust optimization is applied to model this uncertainty in the buyer prosumers model. The proposed decentralized robust optimization guarantees the solution’s existence for each realization of uncertainty components. Furthermore, it performs optimization to realize the hard worse case from uncertainty components. A fully decentralized approach known as the fast alternating direction method of multipliers (FADMM) is employed to solve the proposed decentralized robust problem. The proposed approach does not require third-party involvement as a supervisory node nor disclose the players’ private information. Numerical studies were carried out on a small distribution system with several prosumers. The numerical results suggested the operationality and applicability of the proposed decentralized robust framework and the decentralized solving method

Optimal placement of fuses and switches in active distribution networks using value-based MINLP

Contingency conditions in distribution networks create financial losses for different parts of the system including electricity customers, electricity retailers, distributed generation (DG) units, etc. Therefore, protective device allocation methods have been introduced in recent years to enhance the reliability of the power system. In this study, a new formulation is proposed to find the optimal places of sectionalizing switches and fuses while taking the financial loss of both electricity customers and DG units into account. The current method has the flexibility to consider DG effect on any location of the network and its islanded operation in case of contingencies. Moreover, the uncertainty in load and renewable generation is taken into account using stochastic programming. The results demonstrate that the DG units and their financial loss can change the results of switch and fuse placement dramatically when there are no tie switches in the network. Furthermore, it is found that this method can decrease the total reliability costs by 3.86% when high penetration of DG units is introduced into a modified Roy Billinton test system (RBTS). The problem is modeled as a mixed-integer nonlinear (MINLP) formulation and is handled using BARON solver in GAMS environment

Dynamic stochastic joint expansion planning of power systems, natural gas networks, and electrical and natural gas storage

Over the last decades, electricity generation from natural gas has substantially increased, mostly driven by low natural gas prices due to fracturing and lower extraction costs. The geographic distance between natural gas resources and load centers calls for a holistic tool for joint expansion of power systems and natural gas networks. In this paper, a Dynamic Stochastic Joint Expansion Planning (DSJEP) of power systems and natural gas networks is proposed to minimize the investment and operational costs of power and natural gas systems. Electrical and natural gas storage (ENGS) are considered as an option for decision-makers in the DSJEP problem. The proposed approach takes into account long-term uncertainties in natural gas prices and electric and natural gas demands through scenario realizations. In dynamic planning, more scenario needs more time for computation; therefore, scenario reduction is implemented to eschew unnecessary scenarios. The proposed formulation is implemented on a four-bus electricity system with a five-node natural gas network. To demonstrate the efficiency and scalability of the proposed approach, it is also tested on the IEEE 118-bus system with a 14-node natural gas network. The numerical results demonstrate that ENGS can reduce the total investment cost, up to 52% in the test cases, and operational cost, up to 3%. In this paper, co-planning of power and natural gas systems considering natural gas and electrical storage is represented. Also, electrical and natural gas load growth uncertainties are taken into account to model the real situations. The purpose of the model is to minimize investing and operational costs

Co-Optimization of Energy Losses and Transformer Operating Costs Based on Smart Charging Algorithm for Plug-in Electric Vehicle Parking Lots

The global transport sector has a significant share of greenhouse gas emissions. Thus, plug-in electric vehicles (PEVs) can play a vital role in the reduction of pollution. However, high penetration of PEVs can pose severe challenges to power systems, such as an increase in energy losses and a decrease in the transformers expected life. In this paper, a new day-ahead co-optimization algorithm is proposed to reduce the unwanted effects of PEVs on the power system. The aim of the proposed algorithm is minimizing the cost of energy losses as well as transformer operating cost by the management of active and reactive powers simultaneously. Moreover, the effect of harmonics, which are produced by the charger of PEVs, are considered in the proposed algorithm. Also, the transformer operating cost is obtained from a method that contains the purchase price, loading, and losses cost of the transformer. Another advantage of the proposed algorithm is that it can improve power quality parameters, e.g., voltage and power factor of the distribution network by managing the reactive power. Afterward, the proposed algorithm is applied to a real distribution network. The results show that the proposed algorithm optimizes the daily operating cost of the distribution network efficiently. Finally, the robustness of the proposed algorithm to the number and distribution of PEVs is verified by simulation results

Rate of Change of Direct-Axis Current Component Protection Scheme for Inverter-Based Islanded Microgrids

Rapid growth in the utilization of the inverter-interfaced distributed energy resources (IIDERs) in microgrids has brought new challenges in the network protection area. Microgrid protection specifically becomes a concern during operation in the islanded mode. There is a considerable reduction in fault current levels in this mode compared to when the microgrid is connected to the grid, which makes conventional algorithms operate with significant delay or, in many cases, not even pick up the fault. This paper proposes a protection algorithm based on the rate of change of direct-axis current component ( id ) to protect inverter-based microgrids (IBMGs). The proposed algorithm is applicable for microgrids with centralized protection as well as those deploying a decentralized approach equipped with the unit protection of the relevant lines. Photovoltaic (PV) systems and battery energy storage systems (BESS) are taken into account in this research and modeled precisely to capture the high-frequency effects of power-electronic converters and investigate the response of IIDERs in fault conditions. The effectiveness of the proposed protection method will be evaluated by applying symmetrical and asymmetrical faults in different locations with different resistances simulated on a test IBMG system in PSCAD/EMTDC environment. In addition, protection robustness against non-fault conditions such as a sudden increase in load levels, environmental uncertainties, and noisy measurement conditions will be scrutinized

Overarching Preventive Sympathetic Tripping Approach in Active Distribution Networks Without Telecommunication Platforms and Additional Protective Devices

Nowadays, distributed generation (DG) has made it possible to generate electricity close to the consumption site, resulting in improved efficiency, less environmental pollution, and higher economic profit. These advantages have led to increased penetration of DGs in the distribution system. Protective devices in a distribution system are set by considering the main substation as the only source for feeding short circuit current. However, with the increased influence of DGs as the second main source of short circuit current in distribution systems the short circuit level changes, which leads to false tripping of protective devices, including overcurrent relays. A sympathetic trip, occurring due to a fault in the adjacent feeder, is one of the most serious challenges. This paper analyzes the sympathetic trip in the presence of synchronous based DGs. The equations related to the participation of DGs and upstream network in feeding the short circuit current are obtained. The effect of different parameters on the probability of occurrence of a sympathetic trip is also investigated. Moreover, a novel fast solution is presented for overcoming the sympathetic trip of synchronous based DGs. The proposed method is introduced using the positive-sequence currents of the DGs and main substation. The sympathetic trip is predicted by adopting this prediction index and its occurrence is avoided. The proposed methodology is independent of telecommunication platforms and additional protective devices and can be applied to various short circuits. The method is tested on a network by simulating in DIgSILENT PowerFactory software. Simulation results show the effectiveness of the proposed methodology in predicting and preventing sympathetic trips

A framework for day-ahead optimal charging scheduling of electric vehicles providing route mapping: Kowloon case study

With the ever-increasing growth of electric vehicles (EV)s in the power industry, their significance as a flexible load has increased drastically. On the other hand, uncontrolled charging of these vehicles can cause serious problems in the grid, such as a peak in demand, a decrease in the life expectancy of transformers and as a result, an increase in charging costs of EVs for the EV owners. In this paper, a framework for day-ahead optimal charging of EVs is proposed which through optimization of active and reactive power exchange at each time interval, could prevent the problems mentioned above and at the same time increase the benefit of EV owners and network operators simultaneously. Furthermore, taking into account the effective factors on electrical energy consumption of EVs and the driving pattern of their owners, a route mapping algorithm is developed based on the proposed framework, so as to provide the EV owners with better services. The simulations are carried out using a hybrid interior-point optimization approach, based on traffic and geographic data collected from the city of Kowloon and a standard IEEE 33 bus system is used. The simulation results show that integrating optimal charging of EVs with a route mapping algorithm into the proposed framework can reduce the loss costs of the network during the hours of EVs’ presence in the framework and the selling price of electricity to EV owners by 24.93% and 33.6%, respectively in comparison with the uncontrolled mode. Also, the average life expectancy of power transformers is increased by 2.97% in the optimal charging mode compared to the uncontrolled mode