Abebe W. Bizuayehu

New Multistage and Stochastic Mathematical Model for Maximizing RES Hosting Capacity—Part I: Problem Formulation

This two-part work presents a new multistage and stochastic mathematical model, developed to support the decision-making process of planning distribution network systems (DNS) for integrating large-scale “clean” energy sources. Part I is devoted to the theoretical aspects and mathematical formulations in a comprehensive manner. The proposed model, formulated from the system operators viewpoint, determines the optimal sizing, timing, and placement of distributed energy technologies (particularly, renewables) in coordination with energy storage systems and reactive power sources. The ultimate goal of this optimization work is to maximize the size of renewable power absorbed by the system, while maintaining the required/standard levels of power quality and system stability at a minimum possible cost. From the methodological perspective, the entire problem is formulated as a mixed integer linear programming optimization, allowing one to obtain an exact solution within a finite simulation time. Moreover, it employs a linearized ac network model which captures the inherent characteristics of electric networks and balances well accuracy with computational burden. The IEEE 41-bus radial DNS is used to test validity and efficiency of the proposed model, and carry out the required analysis from the standpoint of the objectives set. Numerical results are presented and discussed in Part II of this paper to unequivocally demonstrate the merits of the model.

Analysis of requirements in insular grid codes for large-scale integration of renewable generation

Large-scale deployment of renewables in island power systems is attracting local attention of grid operators as a way of reducing fuel fossil consumption. Planning a grid based on renewable power plants poses serious challenges to the normal operation of a power system, namely on frequency and voltage stability. Regardless of its inherent problems, there is a consensus that in a future not far away, the green energy could supply most of local needs with less production based on fuel burning. In past grid code compliance, wind turbines did not require services for supporting grid operation. To shift to large-scale integration of renewables, the island grid code should incorporate a new set of requirements in order to regulate the inclusion of these services. Hence, this paper focuses on grid code requirements for large renewable energy integration based distributed generation in island power systems. The paper also discusses additional requirements such as “virtual” wind inertia for improving regulation capability of wind farms and electric energy storage applications for better renewable generation performance. Moreover, a comparative analysis of insular grid code compliance to these requirements in European context is presented.

New Multi-Stage and Stochastic Mathematical Model for Maximizing RES Hosting Capacity—Part II: Numerical Results

A new multistage and stochastic mathematical model of an integrated distribution system planning problem is described in Part I. The efficiency and validity of this model are tested by carrying out a case study on a standard IEEE 41-bus radial distribution system. The numerical results show that the simultaneous integration of energy storage systems (ESSs) and reactive power sources largely enables a substantially increased penetration of variable generation (wind and solar) in the system, and consequently, reduces overall system costs and network losses. For the system, a combined wind and solar PV power of up to nearly three times the base-case peak load is installed over a three-year planning horizon. In addition, the proposed planning approach also considerably defers network expansion and/or reinforcement needs. Generally, it is clearly demonstrated in an innovative way that the joint planning of distributed generation, reactive power sources, and ESSs, brings significant improvements to the system such as reduction of losses, electricity cost, and emissions as a result of increased renewable energy sources (RESs) penetration. Besides, the proposed modeling framework considerably improves the voltage profile in the system, which is crucial for a normal operation of the system as a whole. Finally, the novel planning model proposed can be considered as a major leap forward toward developing controllable grids, which support large-scale integration of RESs.

Novel Multi-Stage Stochastic DG Investment Planning with Recourse

This paper presents a novel multi-stage stochastic distributed generation investment planning model for making investment decisions under uncertainty. The problem, formulated from a coordinated system planning viewpoint, simultaneously minimizes the net present value of costs rated to losses, emission, operation, and maintenance, as well as the cost of unserved energy. The formulation is anchored on a two-period planning horizon, each having multiple stages. The first period is a short-term horizon in which robust decisions are pursued in the face of uncertainty; whereas, the second one spans over a medium to long-term horizon involving exploratory and/or flexible investment decisions. The operational variability and uncertainty introduced by intermittent generation sources, electricity demand, emission prices, demand growth, and others are accounted for via probabilistic and stochastic methods, respectively. Metrics such as cost of ignoring uncertainty and value of perfect information are used to clearly demonstrate the benefits of the proposed stochastic model. A real-life distribution network system is used as a case study and the results show the effectiveness of the proposed model.

Impacts of Stochastic Wind Power and Storage Participation on Economic Dispatch in Distribution Systems

Evaluating the impact related to stochastic wind generation and generic storage on economic dispatch in distribution system operation is an important issue in power systems. This paper presents the analysis of the impacts of high wind power and storage participation on a distribution system over a period of 24 h using grid reconfiguration for electrical distribution system (EDS) radial operation. In order to meet this objective, a stochastic mixed integer linear programming (SMILP) is proposed, where the balance between load and generation has to be satisfied minimizing the expected cost during the operation period. The model also considers distributed generation (DG) represented by wind scenarios and conventional generation, bus loads represented through a typical demand profile, and generic storage. A case study provides results for a weakly meshed distribution network with 70 buses, describing in a comprehensive manner the effects of stochastic wind scenarios and storage location on distribution network parameters, voltage, substation behavior as well as power losses, and the expected cost of the system.

Impacts of Operational Variability and Uncertainty on Distributed Generation Investment Planning: A Comprehensive Sensitivity Analysis

This paper presents a comprehensive sensitivity analysis to identify the uncertain parameters which significantly influence the decision-making process in distributed generation (DG) investments and quantify their degree of influence. To perform the analysis, a DG investment planning model is formulated as a novel multistage and multiscenario optimization problem. Moreover, to ensure tractability and make use of exact solution methods, the entire problem is kept as a mixed-integer linear programming optimization. A real-world distribution network system is used to carry out the analysis. The results of the analysis generally show that uncertainty as well as operational variability of the considered parameters have meaningful impacts on investment decisions of DG. The degree of influence varies from one parameter to another. But, in general, ignoring or inadequately considering uncertainty and variability in model parameters has a quantifiable cost. Hence, the analysis made in this paper can be very useful to identify the most relevant model parameters that need special attention in planning practices.

DG investment planning analysis with renewable integration and considering emission costs

The prospect of distributed generation investment planning (DGIP) is especially relevant in insular networks because of a number of reasons such as energy security, emissions and renewable integration targets. In this context, this paper presents a DGIP model that considers various DG types, including renewables. The planning process involves an economic analysis considering the costs of emissions, reliability and other relevant cost components. In addition, a comprehensive sensitivity analysis is carried out in order to investigate the effect of variability and uncertainty of model parameters on DG investment decisions. The ultimate goal is to identify the parameters that significantly influence the decision-making process and to quantify their degree of influence. The results show that uncertainty has a meaningful impact on DG investment decisions. In fact, the degree of influence varies from one parameter to another. However, in general, ignoring or inadequately considering uncertainty and variability in model parameters has a quantifiable cost. The analyses made in this paper can be very useful to identify the most relevant model parameters that need special attention in planning practices.

NaS battery storage system modeling and sizing for extending wind farms performance in Crete

Crete Island has significant natural resources when it comes to wind and solar energy. Likewise other European territories, renewable sources already are being explored for power production. Currently, a large amount of wind energy on Crete is curtailed during certain daily periods as a result of reduced demand and minimum operating levels of thermal generators. Reducing curtailment losses requires additional sources of flexibility in the grid, and electric energy storage is one of them. This paper address wind generation losses minimization through the storage of wind energy surplus. Sodium Sulfur (NaS) battery modeling is used in this study and an energy time-shift storage scheme is implemented to assess the overall storage system performance. The obtained results are supported on real data of renewable resources (wind and solar), conventional power production and demand of Crete Island in 2011. Conclusions are duly drawn.

Impacts of different renewable energy resources on optimal behavior of Plug-in Electric Vehicle parking lots in energy and ancillary services markets

This paper aims to optimize the behavior of Plug-in Electric Vehicle (PEV) parking lots considering the interaction with renewable energy resources. In the proposed model, the PEV parking lot can participate in the day-ahead energy, spinning reserve and regulation markets. On this basis, behavior of the PEV parking lot in a distribution system is modeled consisting of wind and photovoltaic power producers. Moreover, the model consists of uncertainty characteristics of PEV behavior, wind and photovoltaic power generation. In order to tackle the mentioned uncertainties, a stochastic programming approach is utilized to maximize the profit of the parking lot operator. Numerical cases are studied to show the effectiveness of the proposed model. The results indicate that renewable energy resources have significant impacts on the behavior of PEV parking lot.

A new dynamic and stochastic distributed generation investment planning model with recourse

This paper presents a new dynamic and stochastic decision supporting model for distributed generation investment planning (DGIP). The model is formulated as a mixed integer linear programming (MILP) optimization problem that simultaneously minimizes emission, operation and maintenance, as well as reliability costs. One of the salient features of the model is that it is based on a two-period planning horizon: a short-term planning period that requires robust decisions to be made and a medium to long-term one involving exploratory or flexible investment decisions. Each period has multiple decision stages. The operational variability introduced by intermittent generation sources and electricity demand are accounted for via probabilistic methods. To ensure computational tractability, the associated operational states are reduced via a clustering technique. Moreover, uncertainties related to emission price, demand growth and the unpredictability of intermittent generation sources are taken into account stochastically. A real-life distribution network system is used as a case study, and the results of our analyses generally show the efficacy of the proposed model.