محمودرضا حقی فام

Publicly releasing local electricity markets data brings a multitude of economic as well as technical and societal benefits. Moreover, public access to these data is instrumental for achieving transparency and more competition in local electricity markets. However, privacy-aware market participants have concerns about the leakage of their private information via releasing of the local electricity market outputs. This paper aims to design a mechanism for local electricity markets that provably guarantees the privacy of market participants and also reflects their privacy preferences by implementing differential privacy. First, the required randomization for achieving differential privacy is embedded in the optimization process of the market-clearing problem via noisy gradient ascent algorithm. Then, for providing personalized level of privacy protection, a subsampling mechanism over the input dataset of the market-clearing problem. In numerical studies, the inherent trade-off between the privacy protection and social welfare in the market is investigated under different privacy regimes.

Possessing product technologies is an inevitable necessity for a catch-up. Latecomers must strengthen their design capabilities to enter the innovation stage. A part of the design capabilities is created by developing and applying product technologies. Despite this necessity, most methods and tools for formulating technology strategy focus on process technologies (embedded in the company's value chain) and ignore product technologies. A precise conceptualization of the relationship between the product technologies and the design capabilities is also required. In this article, while separating product and process technologies, the relationship between design capabilities and product technologies is explained. Also, using the "design thinking" approach, a method for formulating a technology strategy is presented, in which both product and process technologies are considered. The implementation of the proposed method is shown with a practical example of 230-320 MW generators. Using the focus group, the validity of the method has been evaluated. The applicability of the method has been shown by implementing it in 21 subsidiaries of the Mapna group. The theoretical contribution and practical application of the research are discussed and further research is suggested.

Privacy-preserving electricity markets have a key role in steering customers towards participation in local electricity markets by guarantying to protect their sensitive information. Moreover, these markets make it possible to statically release and share the market outputs for social good. This paper aims to design a market for local energy communities by implementing Differential Privacy (DP) that provably guarantees the privacy of market participants. Besides achieving a near-optimal solution and preserving the privacy of market participants, the proposed model maintains the utility of the market data for statistical releases. The required randomization for satisfying DP is embedded as Gaussian noise in each iteration of the gradient ascent algorithm underlying the optimization process of the market-clearing problem. In numerical studies, we investigate the impacts of the privacy loss parameter on the output distribution of the market-clearing quantities and payoffs of the market participants. In addition, we demonstrate the inherent trade-off between privacy protection and social welfare in the market under different privacy regimes.

In this paper, a stochastic multi-objective optimization model for planning distribution networks has been proposed. After running this model, the optimal configuration will be obtained. The proposed model has two main objectives (1) Minimizing the Net Present Value and (2) Minimizing the Expected Energy Not Supplied. In this model, based on the optimal solution, the network’s buses and feeders can be either AC or DC. Monte Carlo Simulation (MCS) has been used in order to model stochastic variations of load demands and distributed generations’ output. For obtaining the best solution, the planning problem will be solved with NSGA, and then a tradeoff between NPV and EENS will be calculated with Pareto Front. The proposed model will be tested on a 14-Bus network, which includes AC and DC load points, and DGs with AC or DC output. The test network will be planned as a pure AC network, and also as a hybrid AC/DC network for (1) an urban zone and (2) a case in which some of the load points are industrial loads and have a very large interruption cost. Finally, at the end of the paper, some results such as NPV, ENS, network’s loss, and a reliability index such as SAIDI will be compared.

Electricity losses as an integral part of power delivery systems have always been recognized by electricity industry actors as a measure of how the network managed. In a way, the high losses of the network show the weak managers and the inefficiency of the programs implemented in this network. The exact amount of losses is measured by load flow; But distribution networks, especially low voltage networks, have many information deficiencies due to the expansion and lack of development of information systems; This has left no measure of losses available in this section. Therefore, the electricity network regulator and distribution companies do not have a good view to assessing the management status of the network, the efficiency of the implemented projects, future planning, and budget allocation. In this paper, by using statistical analysis and high responsiveness of deep neural networks to nonlinear problems, a new method for estimating losses in the weak space of information systems is presented.

After a permanent fault occurs if it is not possible to supply the load in the network, the optimal load restoration scheme allows the system to restoration the load with the lowest exit cost, the lowest load interruption, and in the shortest possible time. This article introduces a new design called Smart Load Shedding, abbreviated SLS. In the proposed SLS scheme, the types of devices in smart homes are divided into four categories: adjustable, interruptible, shiftable, and uncontrollable loads. In this design, a new two-layer algorithm is proposed to solve the service restoration problem. In the first layer, a heuristic method based on graph theory for optimal system configuration is presented. In the second layer, for optimal load restoration, tools such as rescheduling of distributed generation sources, load shedding, load curtailment, and sensitive load curtailment are used. This layer is a nonlinear mixed-integer nonlinear programming problem. To achieve the optimal global solution and less solution time, the model changes from nonlinear programming of non-convex mixed integer nonlinear programming to mixed-integer linear programming. A modified RBTS distribution system is used as a test system to demonstrate the effectiveness of the proposed method.

The lack of clarity regarding the participation of transmission system operators and their collaboration with distribution system operators in local flexibility markets is one of the challenges of these markets. So in this study for managing transmission system unpredicted congestions due to sudden increase in system load with demand side resources, a bilevel flexibility market is presented. The first level of this market is related to providing TSOs with required services and only DSOs can participate in this level. The second level consists of local markets in each distribution system that are managed by DSOs and prosumers submit their flexibility bids in these markets. In this paper, DSO and TSO coordination is based on DSO-managed models. This means that DSO aggregates all flexibility bids in distribution networks and modifies prosumers' cost functions considering distribution network loss minimization. The DSO then submits these bids to the first level flexibility market. Modifying prosumers' cost functions is an optimization problem that is solved using water cycle optimization algorithms.

Creating positive characteristics of power generation such as reducing power losses, improving voltage profiles and increasing reliability can be achieved through careful studies and significant costs. Therefore, the study of the mentioned cases and the presentation of optimal solutions has created a new field for research and study in the electricity industry. It should also be noted that most network designers recommend the use of distributed generation resources and capacitive banks in order to solve most of the distribution network problems. Therefore, in this paper, capacitive banks and distributed generation power plants are located simultaneously in the distribution network. The purpose of this placement is to minimize power losses, improve voltage profiles and improve reliability in parallel. The study will be conducted on the IEEE 118-bus network and will be done by designing scenarios based on fault and the number of resources placed. Also, genetic algorithm and forward-backward load flow method have been used as a problem solving technique using MATLAB software. ENS and SAIDI indicators are evaluated to improve the reliability of the island network and then its effect on the studied network is investigated.

Electricity utility have long sought to identify and reduce energy theft, which represents significant part of non-technical losses. On the other hand, once a fraudulent customer is detected, on-site inspection is necessary for final verification. Since inspecting all customers is expensive, utilities seek to reduce the range of inspection to cases with a higher probability of theft. One way to reduce the scope of inspection is to use artificial intelligence-based methods. An essential challenge here is data imbalance in terms of the ratio of normal to fraudulent customers, which leads to the poor performance of algorithms. In This paper in order to overcome this challenge, assuming that suspicious behavior can be expressed as a mathematical function of normal behavior, in the first stage, the consumption pattern of normal and suspicious customers is categorized using clustering algorithms. Then a deep neural network is trained to model suspicious customers. Next, using trained network, possible theft scenarios for normal costumers are predicted. Finally, a secondary deep neural network is trained to separate the normal and suspicious customers. Assessment of the proposed algorithm for different scenarios on a real data-set with more than 6000 customers and comparison with previous research shows its high performance.

After occurrence of a fault and outage of loads in a distribution network, it is tried to reconnect as much as possible loads in the minimum time by a process which is generally known as restoration. Computational and decision-making tools can play an important role to have a successful restoration. One of these tools is multi-agent system, which is a software-based system that includes components or agents. These agents have the ability to exchange information and make local decisions. In this paper, a multi-agent system is proposed for restoration of radial distribution networks. The multi-agent system brings about decentralized management in the restoration. Also, the proposed system considers priority for zones in distribution feeders. The priority is in accordance with type of loads in the zones. Implementation of the proposed method using Java Agent Development Framework (JADE) and its evaluation on a test distribution network, demonstrates an effective restoration with consideration of load priorities.

Power systems have been traditionally designed and operated based on the main principles of reliability, i.e. security and adequacy. These principles can properly deal with the known failures in the power infrastructure components. However, recently occurred High Impact Low Probability (HILP) threats have raised the concerns about the classical reliability-oriented view. Hence, resilient distribution networks against various threats and enhancement of energy security in critical situations are the most significant challenges that must be taken into consideration. Distribution network plays vital role in power systems because of its connection point to the end users and due to its wide range, security of electricity supply is strongly dependent on it. Hence, a lot of efforts have been done on distribution networks scheduling, planning and operation. So, optimal resilient planning of conventional medium voltage distribution network is aimed at mitigating vulnerability to natural disasters. In this paper, a new method is suggested to make a relation between network component fragility curves, component geographical location and natural disasters spatial risk index. To achieve this goal, the resiliency concept and effective solutions for resiliency increment, elicitation of vulnerability atlas of distribution networks and natural disaster risk based modelling have been discussed and assessed comprehensively in this paper. The simulation of proposed method is applied on a large scale distribution network. The obtained results validate the effectiveness of proposed methodology.

One of the most important tasks of operators in distribution companies is to restoration after a fault in the network. In load restoration schemes, in addition to observing the load flow constraints, it is necessary to maintain the grid structure radially and, most importantly, observing the balance of consumption with the possibility of providing load due to the limitations of the backup feeder (or distributed resource constraints). This paper presents a new approach called smart load shedding in distribution networks, which aims to design a smart load shedding module in situations where it is not possible to provide load after a network failure. In this case, the network load restoration will be provided at the lowest outage cost and in the shortest possible time to maximize customer satisfaction. In order to validate the proposed method, the level of automation of the sample distribution network for smart control of loads is defined in three scenarios. The types of loads available in smart homes are also prioritized and categorized into three categories of adjustable, interruptible, and shiftable loads. The proposed method is coded in the MATLAB software environment. To demonstrate the effectiveness of the proposed model, simulation is implemented on an RBTS system. The results show the effectiveness of the proposed method in reducing costs and improving the restoration management of the distribution system.

Self-healing as one of the most important features of smart distribution networks that increases network reliability and resiliency. Considering the necessity of studying the challenges facing outage management in smart distribution networks and developing the proposed solutions in this field, this paper investigate the impact of self-healing and automatic outage management on smart distribution networks on network reliability. In this paper, a new algorithm is proposed to evaluate the reliability of smart distribution networks by describing the problem scope and providing a control and operation model in both normal and self-healing modes. In the proposed algorithm, the Monte Carlo method is used to simulate equipment outage scenarios. In each outage scenario, in the optimization problem framework, the status of the controllable switch  to create the structure of the multi microgrid, direct load control program and the scheduling of thermal sources and energy storage in the fault period are determined. The capabilities of the proposed model are to consider the wind resources uncertainties and the distribution network operational constraint such as voltage constraints and line thermal constraints. The results of the implementation of the proposed algorithm on the modified IEEE RBTS network will improve the reliability of the network by adopting outage management strategies in smart distribution networks.

In this paper a joint energy and reserve market with transmission switching considering dynamic constraints has been proposed to minimize the cost of supplying load, reliability expenses and avoidance of transient instability in line switching.Transmission switching is used during contingencies and steady state to determine optimal required energy and reserve values. To investigate the efficiency of the proposed strategy IEEE 14 bus test is studied. According to the obtained results, this strategy decreases energy and reserve marginal prices, as well as reliability cost.