arXiv subjects

Mohammad Rasoul Narimani

Publications and source records attributed to Mohammad Rasoul Narimani.

26 records · Page 2Linked to original sources

arXiv2021

Toward Efficient Wide-Area Identification of Multiple Element Contingencies in Power Systems

Power system N-x contingency analysis has inherent challenges due to its combinatorial characteristic where outages grow exponentially with the increase of x and N. To address these challenges, this paper proposes a method that utilizes Line Outage Distribution Factors (LODFs) and group betweenness centrality to identify subsets of critical branches. The proposed LODF metrics are used to select the high-impact branches. Based on each selected branch, the approach constructs the subgraph with parameters of distance and search level, while using branches' LODF metrics as the weights. A key innovation of this work is the use of the distance and search level parameters, which allow the subgraph to identify the most coupled critical elements that may be far away from a selected branch. The proposed approach is validated using the 200- and 500-bus test cases, and results show that the proposed approach can identify multiple N-x contingencies that cause violations.

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arXiv2021

Mixed-Integer Optimization for Bio-Inspired Robust Power Network Design

Power systems are susceptible to natural threats including hurricanes and floods. Modern power grids are also increasingly threatened by cyber attacks. Existing approaches that help improve power system security and resilience may not be sufficient; this is evidenced by the continued challenge to supply energy to all customers during severe events. This paper presents an approach to address this challenge through bio-inspired power system network design to improve system reliability and resilience against disturbances. Inspired by naturally robust ecosystems, this paper considers the optimal ecological robustness that recognizes a unique balance between pathway efficiency and redundancy to ensure the survivability against disruptive events for given networks. This paper presents an approach that maximizes ecological robustness in transmission network design by formulating a mixed-integer nonlinear programming optimization problem with power system constraints. The results show the increase of the optimized power system's robustness and the improved reliability with less violations under N-x contingencies.

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arXiv2020

Generalized Contingency Analysis Based on Graph Theory and Line Outage Distribution Factor

Identifying the multiple critical components in power systems whose absence together has severe impact on system performance is a crucial problem for power systems known as (N-x) contingency analysis. However, the inherent combinatorial feature of the N-x contingency analysis problem incurs by the increase of x in the (N-x) term, making the problem intractable for even relatively small test systems. We present a new framework for identifying the N-x contingencies that captures both topology and physics of the network. Graph theory provides many ways to measure power grid graphs, i.e. buses as nodes and lines as edges, allowing researchers to characterize system structure and optimize algorithms. This paper proposes a scalable approach based on the group betweenness centrality (GBC) concept that measures the impact of multiple components in the electric power grid as well as line outage distribution factors (LODFs) that find the lines whose loss has the highest impact on the power flow in the network. The proposed approach is a quick and efficient solution for identifying the most critical lines in power networks. The proposed approach is validated using various test cases, and results show that the proposed approach is able to quickly identify multiple contingencies that result in violations.

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arXiv2020

Tightening QC Relaxations of AC Optimal Power Flow Problems via Complex Per Unit Normalization

Optimal power flow (OPF) is a key problem in power system operations. OPF problems that use the nonlinear AC power flow equations to accurately model the network physics have inherent challenges associated with non-convexity. To address these challenges, recent research has applied various convex relaxation approaches to OPF problems. The QC relaxation is a promising approach that convexifies the trigonometric and product terms in the OPF problem by enclosing these terms in convex envelopes. The accuracy of the QC relaxation strongly depends on the tightness of these envelopes. This paper presents two improvements to these envelopes. The first improvement leverages a polar representation of the branch admittances in addition to the rectangular representation used previously. The second improvement is based on a coordinate transformation via a complex per unit base power normalization that rotates the power flow equations. The trigonometric envelopes resulting from this rotation can be tighter than the corresponding envelopes in previous QC relaxation formulations. Using an empirical analysis with a variety of test cases, this paper suggests an appropriate value for the angle of the complex base power. Comparing the results with a state-of-the-art QC formulation reveals the advantages of the proposed improvements.

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arXiv2019

Demand Side Management for Homes in Smart Grids

Electricity usage is a major portion of utility bills and the best place to start lowering them. An effective home energy management approach is introduced to decrease customers' electricity bills by determining the optimal appliance scheduling under hourly pricing based demand response (DR) strategies. The proposed approach specifically addresses consumer comfort through acceptable appliance deferral times, within which their consumption can be shifted from the normal schedules. Electric vehicles (EVs), with vehicle to grid (V2G) and vehicle-to-home (V2H) abilities, Energy storage systems (ESS), and Renewable Energy Resource as a distributed generation are considered at the end-user premises to improve the energy management efficiency. A mixed-integer linear programming (MILP) framework is implemented to solve the proposed problem. Numerical results show that the proposed approach can reduce customers' electricity bill without sacrificing their comfort and fully exploit EVs and ESS capacities.

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arXiv2019

An Efficient Scenario-based Stochastic Energy Management of Distribution Networks with Distributed Generation, PV Module, and Energy Storage

Incorporating Renewable Energy Sources (RES) incurs a high level of uncertainties to electric power systems. This level of uncertainties makes the conventional energy management methods inefficient and jeopardizes the security of distribution systems. In this connection, a scenario-based stochastic programming is introduced to harness uncertainties in the load, electricity price, and photovoltaic generation. Further, a hybrid evolutionary algorithm based on Grey Wolf Optimizer and Particle Swarm Optimisation algorithm is proposed to find the best operation cost, and Energy Not Supplied (ENS) as two important objective functions, which almost always are in stark contrast with each other. The proposed algorithm is applied to the modified IEEE 69-bus test system and the results are validated in terms of efficiency, which indicates a cogent trade-off between the fitness functions addressed above.

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arXiv2018

Empirical Investigation of Non-Convexities in Optimal Power Flow Problems

Optimal power flow (OPF) is a central problem in the operation of electric power systems. An OPF problem optimizes a specified objective function subject to constraints imposed by both the non-linear power flow equations and engineering limits. These constraints can yield non-convex feasible spaces that result in significant computational challenges. Despite these non-convexities, local solution algorithms actually find the global optima of some practical OPF problems. This suggests that OPF problems have a range of difficulty: some problems appear to have convex or "nearly convex" feasible spaces in terms of the voltage magnitudes and power injections, while other problems can exhibit significant non-convexities. Understanding this range of problem difficulty is helpful for creating new test cases for algorithmic benchmarking purposes. Leveraging recently developed computational tools for exploring OPF feasible spaces, this paper first describes an empirical study that aims to characterize non-convexities for small OPF problems. This paper then proposes and analyzes several medium-size test cases that challenge a variety of solution algorithms.

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arXiv2018

Improving QC Relaxations of OPF Problems via Voltage Magnitude Difference Constraints and Envelopes for Trilinear Monomials

AC optimal power flow (AC~OPF) is a challenging non-convex optimization problem that plays a crucial role in power system operation and control. Recently developed convex relaxation techniques provide new insights regarding the global optimality of AC~OPF solutions. The quadratic convex (QC) relaxation is one promising approach that constructs convex envelopes around the trigonometric and product terms in the polar representation of the power flow equations. This paper proposes two methods for tightening the QC relaxation. The first method introduces new variables that represent the voltage magnitude differences between connected buses. Using "bound tightening" techniques, the bounds on the voltage magnitude difference variables can be significantly smaller than the bounds on the voltage magnitudes themselves, so constraints based on voltage magnitude differences can tighten the relaxation. Second, rather than a potentially weaker "nested McCormick" formulation, this paper applies "Meyer and Floudas" envelopes that yield the convex hull of the trilinear monomials formed by the product of the voltage magnitudes and trignometric terms in the polar form of the power flow equations. Comparison to a state-of-the-art QC implementation demonstrates the advantages of these improvements via smaller optimality gaps.

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