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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.

Crete Island is rich in renewable energy resources such as wind and solar. 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 on thermal generators. Reducing wind power curtailment magnitude requires additional sources of flexibility in the grid, and electric energy storage is one of them. This paper address wind generation curtailment minimization through the storage of wind energy surplus. NaS (sodium sulfura) battery modelling is used in this study in order to shift wind generation from off-peak to on-peak through a technical-economic analysis, considering the total annualized cost of the storage system and the wind power curtailment based on an annual basis. The obtained results are based on real data, which includes Crete Island demand, renewable and conventional power generation.

Nowadays, with the large-scale penetration of distributed and renewable energy resources, ES (energy storage) stands out for its ability of adding flexibility, controlling intermittence and providing back-up generation to electrical networks. It represents the critical link between the energy supply and demand chains, being a key element for increasing the role and attractiveness of renewable generation into the power grid, providing also numerous technical and economic benefits to the power system stakeholders. On islanded systems and micro-grids, being updated about the state-of-the-art of ES systems and their benefits becomes even more relevant. Hence, in the present paper a comprehensive study and analysis of ES leading technologies' main assets, research issues, global market figures, economic benefits and technical applications is provided. Special emphasis is given to ES on islands, as a new contribution to earlier studies, addressing their particular requirements, the most appropriate technologies and existing operating projects throughout the world.

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 operator's 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.

This chapter aims to provide an overview of renewable energy systems (RESs) and the underlying issues related to the RES theme such as climate change and its mitigation. The types of RESs are also briefly discussed focusing on their characteristics and technological aspects. This is followed by the most important economic aspects as well as the challenges and opportunities of integrating RESs in power systems. This chapter also discusses the need for optimization tools adequately equipped to effectively capture the intrinsic characteristics of RESs and facilitate their optimal integration in power systems. All this leads to an efficient exploitation of their wide-range benefits while sufficiently minimizing their negative impacts. Finally, the chapter is summarized with some concluding remarks.

This work presents a new integrated multistage and stochastic mathematical model, which is developed to support the decision-making process related to the expansion planning of distribution network systems for integrating large-scale distributed “clean” energy sources. The developed model, formulated from the distribution system operator's point of view, determines the optimal sizing, time, and placement of distributed energy technologies (renewables, in particular) as well as that of energy storage systems (ESSs) and compensators in distribution networks. The ultimate goal of this optimization work is to maximize the size of distributed generation (DG) power absorbed by the system while maintaining the power quality and stability at the required/standard levels at a minimal cost possible. The model, formulated as a mixed-integer linear programming optimization, employs a linearized alternating current network model that captures well the inherent characteristics of power network systems, and balances accuracy with computational burden. The standard IEEE 41-bus distribution system is used to test the developed model and carry out the required analysis from the standpoint of the objectives set. The results of the case study show that the integration of ESS and compensators helps to significantly increase the size of variable generation (wind and solar) in the system. For the case study, a total of 10 MW demand wind and solar power has been added to the system. One can put this into perspective with the peak load 4.635 MW in the system. This means it has been possible to integrate renewable energy source (RES) power more than twice the peak demand in the base case. It has been demonstrated that the joint planning of DGs, compensators, and ESSs, proposed in this work, bring about significant improvements to the system, such as reduction of losses, cost of electricity and emissions, voltage support, and many more. The expansion planning model proposed here can be considered a major leap forward toward developing controllable grids, which support large-scale integration of RESs (as opposed to the conventional “fit and forget” approach). It can also be a handy tool to speed up the integration of more RESs until smart-grids are materialized in the future.

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

This article presents the impacts of different storage technologies, their size as well as their location on the economic dispatch of a distribution system. The participation of the storage in distribution systems is evaluated over a period of 24 hours using a radial reconfiguration of the network. In order to meet this objective, a stochastic mixed integer linear problem (SMILP) is proposed, where the expected cost during the operation period is minimized. The model considers uncertainties in wind generation, load which is represented through a typical demand profile, conventional generation and storage systems. Results from a case study is presented for a distribution network with 28 buses, comprehensively describing the impacts of the location and the size of the storage system on the distribution network, as well as on the expected operation costs of the system.

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.

One of the major changes currently involving distribution network systems (DNSs) is the ever-increasing integration of renewable-based distributed generation (DG), wind and solar PV types in particular. This is dramatically influencing the planning and operation of distribution systems, in general. The traditional “fit-and-forget” approach is outdated. Current developments in the DNS would require new, efficient and robust planning and operation tools to support smooth integration of such DGs. The present work focuses on an optimal integration of renewable-based DGs with reactive power support capabilities. Accordingly, a stochastic mixed integer linear programming (S-MILP) model is developed that takes into account the optimal integration of RES-based DGs and reactive power sources. The developed model is tested using a standard IEEE distribution system. Test results show that integrating DGs with reactive power support capability significantly enhances voltage stability and improves the overall cost in the system. Simulation results show that setting the reactive power support capability of the RES-based DGs from 0.95 leading to 0.95 lagging leads to the maximum penetration level of wind and solar PV power in the system.

Introduction of renewable energy sources (RES) in insular areas is growing on different islands of various regions in the world and the large-scale deployment of renewables in island power systems is appealing to local attention of grid operators as a method to decrease fossil fuel consumption. Planning a grid based on renewable power plants (RPP) presents serious challenges to the normal operation of a power system, precisely on voltage and frequency stability. Despite of its inherent problems, there is a consensus that in near future the RES could supply most of local needs without depending exclusively on fossil fuels. In previous grid code compliance, wind turbines did not required services to support grid operation. Thus, in order to shift to large-scale integration of renewables, the insular grid code ought to incorporate a new set of requirements with the intention of regulating the inclusion of these services. Hence, this paper discusses grid code requirements for large-scale integration of renewables in an island context, as a new contribution to earlier studies. The current trends on grid code formulation, towards an improved integration of distributed renewable resources in island power systems, are addressed. The paper also discusses advanced grid code requirement concepts such as virtual wind inertia and synthetic inertia for improving regulation capability of wind farms and the application of energy storage systems (EES) for enhancing renewable generation integration. Finally, a comparative analysis of insular grid code compliance to these requirements is presented in the European context.

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.

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.

In this paper, the impacts of type, size and placement of renewable energy resources (RERs) on the offering strategy of Plug-in Electric Vehicle (PEV) parking lots is investigated. To this end, a stochastic programming model is proposed to optimize the behavior of a PEV parking lot in energy and reserve markets, considering the interaction with RESs. On this basis, the PEV parking lot is modeled in a distribution system consisting of wind and photovoltaic power producers. Moreover, the stochastic model consists of uncertainty characteristics of PEV owners' behavior, wind and photovoltaic power generation. Several numerical studies are analyzed to indicate how the factors of the RESs affect the optimal offering strategy of the parking lots.

The idea behind the present work is the application of a Mixed Integer Linear Programming (MILP) model for the reconfiguration and performance analysis of weakly-meshed distribution networks with emphasis on the variability of demand and the integration of wind power generation at specific buses. The ultimate goal is the minimization of active power losses for the corresponding optimal topology in the short-term operation considering key distribution parameters and power factor constraints. A distribution system case study with twenty wind scenarios is considered to evaluate the impact of wind integration in distribution networks. The effectiveness of a simplified linearized model for the study of active power losses and the impacts of wind generation and periodic demand variation on node voltages, branch currents and grid reconfigurations under normal operation conditions is illustrated by the case study. Issues resulting from solving a 25-bus case study are presented and discussed.

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.

This paper presents an assessment of baseline and higher order grid security criterion applications on current power grid systems, with a focus on their implications in an island context. It considers the European grid code requirements for N-1 and higher order criteria as essential elements for the future deployment of insular distribution grid systems to improve security and stability. Consequently, in the opening section, major facts about island grid topology and current security challenges are illustrated based on a literature review. Afterwards, different approaches applied for security assessment are discussed. A summary of optimal power flow (OPF) approaches is given based on classic problem formulation, and a distribution grid security constrained assessment algorithm is proposed and assessed for island grid context.

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.

A successful deployment of electrical energy storage (EES) in current electricity grid systems is a plausible episode in several power systems given the outstanding technical, economic and environmental benefits that EES provides. Also, more distributed resources are becoming key actors in remotely located renewable energy based and poorly interconnected island grid systems. Healing these fragile grid systems in islands requires devising and promoting a robust grid system operation. Such move should go hand in hand with an increasing distributed energy resource use to guarantee sustainable renewable energy integration into these systems. In the present document, EES technologies and applications have been compiled, where special emphasis has been given on islands, studying their particular requirements and technology appropriateness on operating project experiences around the world, from which some lessons can be learned. Conclusions about EES technologies' general suitability on island systems are duly drawn throughout the content of this paper.

Nowadays, with the large-scale penetration of distributed and renewable energy resources, Electrical Energy Storage (EES) stands out for its ability of adding flexibility, controlling intermittence and providing back-up generation to electrical networks. It represents the critical link between the energy supply and demand chains and, moreover, a key element for increasing the role and attractiveness of renewable generation into the power grid, providing numerous technical and economic benefits to the power system stakeholders. On islanded systems and micro-grids, being updated about the state-of-the-art of EES systems and their benefits becomes even more relevant. Hence, in the present paper a comprehensive analysis of EES leading technologies' main assets, research issues, global market figures, economic benefits and technical applications is provided.