Diagonal Quadratic Approximation for Decentralized Collaborative TSO+DSO Optimal Power Flow

Mohammadi, Ali; Mehrtash, Mahdi; Kargarian, Amin

doi:10.1109/tsg.2018.2796034

Cited by 147 publications

(92 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For coordination, diagonal quadratic approximation and truncated quadratic approximation are presented in order to solve optimal power flow in parallel. For validation of the proposed system, a 6-bus and the IEEE 118-bus test systems were used [26]. Authors in [27] proposed a self-decision-making method for load management using a multi-agent system while considering distributed generation and demand response resources in order to reduce the peak load of a distribution network feeder.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units

et al. 2018

View full text Add to dashboard Cite

Abstract:With the rapid advancement in technology, electrical energy consumption is increasing rapidly. Especially, in the residential sector, more than 80% of electrical energy is being consumed because of consumer negligence. This brings the challenging task of maintaining the balance between the demand and supply of electric power. In this paper, we focus on the problem of load balancing via load scheduling under utility and rooftop photovoltaic (PV) units to reduce electricity cost and peak to average ratio (PAR) in demand-side management. For this purpose, we adopted genetic algorithm (GA), binary particle swarm optimization (BPSO), wind-driven optimization (WDO), and our proposed genetic WDO (GWDO) algorithm, which is a hybrid of GA and WDO, to schedule the household load. For energy cost estimation, combined real-time pricing (RTP) and inclined block rate (IBR) were used. The proposed algorithm shifts load from peak consumption hours to off-peak hours based on combined pricing scheme and generation from rooftop PV units. Simulation results validate our proposed GWDO algorithm in terms of electricity cost and PAR reduction while considering all three scenarios which we have considered in this work: (1) load scheduling without renewable energy sources (RESs) and energy storage system (ESS), (2) load scheduling with RESs, and (3) load scheduling with RESs and ESS. Furthermore, our proposed scheme reduced electricity cost and PAR by 22.5% and 29.1% in scenario 1, 47.7% and 30% in scenario 2, and 49.2% and 35.4% in scenario 3, respectively, as compared to unscheduled electricity consumption.

show abstract

Section: Related Workmentioning

confidence: 99%

“…At each iteration velocity and position, values must be updated. Using Equation (26), the new position of the air parcel for each iteration is defined as:…”

Section: Parameters Valuesmentioning

confidence: 99%

Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units

et al. 2018

View full text Add to dashboard Cite

show abstract

“…GWO is a robust swarm-based optimizer inspired by the social hierarchy of grey wolves [27]. The pack of grey wolves has a special social hierarchy where the leadership in the pack can be divided into four levels; alpha, beta, omega and delta.…”

Section: Grey Wolf Optimizermentioning

confidence: 99%

“…In addition, these methods may fall into local minima, hence new optimization algorithms have been proposed to avoid the shortcomings of these methods. From these methods; GA [8,9], MFO [10], DE [11,12], PSO [13], MSA [14], EP [15,16], ABC [17], GSA [18], BBO [19], SFLA [20], forced initialized differential evolution algorithm [21], TS [22], MDE [23], SOS [24], BSA [25] and TLBO [26], decentralized decision-making algorithm [27]. The thermal generation units have multiple valves to control the output generated power.…”

Section: Introductionmentioning

confidence: 99%

Solving Non-Smooth Optimal Power Flow Problems Using a Developed Grey Wolf Optimizer

et al. 2018

View full text Add to dashboard Cite

Abstract:The optimal power flow (OPF) problem is a non-linear and non-smooth optimization problem. OPF problem is a complicated optimization problem, especially when considering the system constraints. This paper proposes a new enhanced version for the grey wolf optimization technique called Developed Grey Wolf Optimizer (DGWO) to solve the optimal power flow (OPF) problem by an efficient way. Although the GWO is an efficient technique, it may be prone to stagnate at local optima for some cases due to the insufficient diversity of wolves, hence the DGWO algorithm is proposed for improving the search capabilities of this optimizer. The DGWO is based on enhancing the exploration process by applying a random mutation to increase the diversity of population, while an exploitation process is enhanced by updating the position of populations in spiral path around the best solution. An adaptive operator is employed in DGWO to find a balance between the exploration and exploitation phases during the iterative process. The considered objective functions are quadratic fuel cost minimization, piecewise quadratic cost minimization, and quadratic fuel cost minimization considering the valve point effect. The DGWO is validated using the standard IEEE 30-bus test system. The obtained results showed the effectiveness and superiority of DGWO for solving the OPF problem compared with the other well-known meta-heuristic techniques.

show abstract

“…The model is useful for forecasting electric power demand from short to medium term horizon. The authors in [7] presented a analytical target cascading based distributed solution for power flow at transmission system operator and distribution system operator. The work in [8] studied the impact of uncertainty in pricing on energy hubs.…”

Section: Introductionmentioning

confidence: 99%

Achieving Cost Minimization and Fairness in Multi-Supplier Smart Grid Environment

et al. 2018

View full text Add to dashboard Cite

Abstract:In this paper, we study the energy management techniques in the smart grid with multiple energy providers. We seek to minimize the electricity cost. In this paper, the desired objectives are achieved through scheduling of different consumers to different utilities at different time slots. We consider a practical system where multiple users can be allocated to a single utility, but, a user cannot be assigned to more than one utility. As a first goal, we formulate a sum cost minimization problem subject to independent generation capacity of each utility. A dual decomposition approach is exploited to find an efficient solution where the sub-gradient approach is adopted to update the dual variables. Later, a min-max based optimization framework is adopted to achieve the fairness among different customers. Moreover, suboptimal schemes are also designed to reduce the computational complexity. Simulation results are presented to validate the performance of the proposed solutions.

show abstract

Diagonal Quadratic Approximation for Decentralized Collaborative TSO+DSO Optimal Power Flow

Cited by 147 publications

References 40 publications

Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units

Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units

Solving Non-Smooth Optimal Power Flow Problems Using a Developed Grey Wolf Optimizer

Achieving Cost Minimization and Fairness in Multi-Supplier Smart Grid Environment

Contact Info

Product

Resources

About