Application of intelligent control agents in power systems with distributed generators

Guest Editorial: Situational awareness of integrated energy systems

This paper reports the results from a survey conducted in 2010/2011 among 14 Norwegian distribution system operators (DSOs) concerning their experiences with integration of distributed generation (DG) in distribution networks. The survey shows what kind of problems DSOs experience caused by DG connected to their networks, how the DSOs approach the problems and what kind of planning methodologies and tools the DSOs are using to overcome the problems. The results of the survey show that most of the DSOs have experienced problems with DG units connected to their network, despite the network reinforcements that have been necessary to integrate many of the DG units. The survey also shows that several of the DSOs are expecting a considerable increase in the number of DG units in their distribution networks in the years to come. This increase will probably be accelerated by the introduction of green certificates by 01.01.2012[1], which is expected to increase the profitability of many DG projects. Some of the DSOs have started to address the challenges in their planning methodologies, improving their planning procedures in order to optimize the DG integration process and decrease the need of expensive grid reinforcements. Still, only a few go beyond the normal planning procedures for networks without DG.

Chapter 9 - Distributed generation in deregulated energy markets and probabilistic hosting capacity decision-making challenges

Insertion of distributed generation (DG) in existing distribution system has effectively improved its performance and operation. Many different approaches for the planning of distribution system with DG insertion are presented by researchers. In this paper a multi objective approach has been proposed to maximize the mutual benefits of both the distribution system operator and DG owner. The contradictory relationship between reduction in MVA rating of DGs and reduction of power losses of the system is the motivation for this multi-objective approach. The best compromised size of DGs in MVA, their operating power factors and positions are obtained to reduce the system active power loss along with the reduction of DGs size. The 69-bus and 85-bus radial distribution system are considered as test systems. The Pareto-front of non-dominated solutions is obtained by using multi-objective differential evolution (MODE) optimization algorithm. The performance of MODE algorithm is also compared with that of multi-objective particle swarm optimization (MOPSO) algorithm. The different system operating indices such as active power loss, reactive power loss and voltage deviation are evaluated to show the effect of the best compromised solution of DGs placement in distribution system.

Power systems have been undergoing radical changes in recent years, and their planning and operation will be surely undertaken according to the Smart Grid (SG) vision in the near future. The SG initiatives aim at introducing new technologies and services in power systems, to make the electrical networks more reliable, efficient, secure and environmentally-friendly. In particular, it is expected that communication technologies, computational intelligence and distributed energy sources will be widely used for the whole power system in an integrated fashion. In particular, nowadays, unprecedented challenges like as stringent regulations, environmental concerns, growing demand for high quality, reliable electricity and rising customer expectations are forcing utilities to rethink about electricity generation and delivery from the bottom up. Moreover, the availability of low cost computing and telecommunications technologies, new generation options, and scalable, modular automation systems push utilities to be dynamic, innovative and ambitious enough to take advantage of them. Driven by the dynamics of the new energy environment, leading utilities, technology vendors and government organizations have created a vision of the next generation of energy delivery systems: the Smart Grid. Operational changes of the grid, caused by restructuring of the electric utility industry and electricity storage technology advancements, have created an opportunity for storage systems to provide unique services to the evolving grid. Especially Battery Energy Storage Systems (BESSs), thanks to the large number and variety of services they can provide, are powerful tools for the solution of some challenges that future grids will face. This consideration makes BESSs critical components of the future grids. The BESS can be applied for different services into the different levels of power system chain to satisfy technical challenges and provide financial benefits. In the context of the application of BESSs in SGs, there are two main problems that need to be addressed in a way that exploits the BESS potential, that are linked to their operation and sizing. This thesis focuses on both these aspects, proposing new strategies that allow optimizing the BESS adoption. When dealing with BESSs, sizing and operation are strictly linked. The correct sizing of a BESS, in fact, needs to take into account its operation which in turn will be effected with the aim of optimizing the whole system where it is included. In the first part of this research study, advanced optimal operating strategies were proposed for BESSs by considering both the distribution system operator perspective and the end user. Thus, the proposed operating strategies were performed with the aim of (i) leveling the active power requested by the loads connected to a distribution system (distribution system operator service), (ii) reducing the electricity costs sustained by an end-use costumer that provides demand response (DR) (end user service) and (iii) scheduling a microgrid (µG) with DR resources such as Plug-in Electric Vehicles (PEVs) and Data Centers (DCs) (both the two section service). The proposed strategies also satisfied technical constraints of BESSs and other components of the µG. The second part of the thesis presented the optimal sizing of BESSs aimed at maximizing the benefits related to their use. In the thesis, the sizing, which is performed by considering the end user point of view with reference to both the industrial and residential customers, is effected by adopting both deterministic and probabilistic approaches. With reference to the deterministic approach, a simple and quick closed form procedure for the sizing of BESSs in residential and industrial applications was proposed. In case of probabilistic approach, the case of a BESS installed in an industrial facility was considered and the sizing was performed based on the decision theory. Technical improvements and economic benefits of optimal operation and optimal sizing of BESSs in SG are demonstrated by the obtained results which are reported in the numerical applications. More specifically, it was clearly determined that BESSs can offer technical supports into the distribution operator section of the grid in terms of load management and security challenges. Moreover optimal integration of BESSs into the grid was also appealing for end users thanks to valuable amounts of electricity bill cost reduction. Regarding the original contribution of the thesis, the following considerations can be done. With reference to the load leveling service, an innovative two-step procedure (day-ahead scheduling and very short time predictive control) was proposed which optimally controls a BESS connected to a distribution substation in order to perform load leveling. In case of DR, a proper control of the BESS was proposed in order to perform DR under different price schemes, such as Real Time Pricing (RTP) and Time of Use (TOU) without modifying the daily work cycle of the industrial loads. The control procedure allows achieving contemporaneously two important goals that are the reduction of the bill costs and the prolonging the battery's lifetime so further reducing the costs sustained by the customer. With reference to the scheduling of microgrids, the original contribution of the thesis is focused on the proposal of optimization strategies aimed at managing and coordinating, simultaneously, batteries on board of vehicles or equipping data centers' Uninterruptable Power Supply (UPS) and Distributed Generation (DG) units. Also comparisons among different single-objective based strategies are made in order to highlight the most convenient. With reference to the sizing based on deterministic approach, unlike the other relating literature, the innovative contribution is that the closed form procedure takes into account both the technical constraints of the battery and contractual agreements between the customer and the utility. Moreover, in the economical analysis performed for the sizing, which is applied with reference to both residential and small industrial customers and is based on actual TOU tariffs, a wide sensitivity analysis to consider different perspectives in terms of life span and future costs was performed. Some aspects that affect the profitability of the battery, such as technological limitations (e.g. the battery and converter efficiency), economic barriers (e.g. capital cost and the rate of change of the cost) and variation of the load profile along the years were deeply analyzed. In case of sizing based on probabilistic approach, the original contributions of the thesis are mainly referred to the proposal of a new method that uses a decision theory-based process to obtain the best sizing alternative considering the various uncertainties affecting the sizing procedure. The thesis is organized in three chapters which are dealing with integration of BESSs in SGs. The first chapter reports basic concepts and characteristics of BESSs, fundamental components and features of SGs and different services that BESSs can provide. The optimal operation strategies of BESS are considered in second chapter which includes their problem formulation, solving procedures and results. The third chapter deals with the optimal sizing problem of BESSs for which the problem formulation, solving procedures and results are reported. Finally, the conclusions are presented in the last part of thesis.

Efficient reduction of power losses by allocating various DG types using the ZOA algorithm

Variations of load demands and generations bring multiple uncertainties to power system operation. Under this situation, power flows become increasingly uncertain, especially when significant distributed generations (DGs), such as wind and solar, are integrated into power systems. In this paper, a Complex Affine arithmetic based unbalanced Three-phase Forward-Backward Sweep power flow model (CATFBS) is proposed to study the impacts of uncertainties in unbalanced three-phase distribution systems. An index of Relative Influence of Uncertain Variables on Outcomes (RIUVO) is proposed for quantifying the impacts of individual uncertain factors on power flows and bus voltages. The CATFBS method is tested on the modified IEEE 13-bus system and a modified 292-bus system. Numerical results show that the proposed method outperforms the Monte Carlo method for exploring the impacts of uncertainties on the operation of distribution systems. The proposed CATFBS method can be used by power system operators and planners to effectively monitor and control unbalanced distribution systems under various uncertainties.

The incentive to operate less polluting energy sources and the electricity liberalization has opened opportunities for the increasing penetration of distributed generation (DG) in the power system. Due to the locally available resources and small scale, DG units are mostly connected at the distribution level. This does not only have impact on the distribution system operation, but on the transmission system as well. In this study, the impact of DG on N-1 safety (contingency) of the transmission system is studied in detail with different DG technologies at given penetration levels.

This paper discusses and identifies the effect of Distributed Generation (DG) units on delivery loss reduction and efficiency improvement of future electric systems. It demonstrates that optimal placement and utilization of DGs in distribution feeders and radial distribution networks can lead to significant efficiency improvement through different parts of electric power systems. To this end, Utilities and Distribution System Operators (DSO) need to encourage DG owners to participate in optimization process. This can be done by implementing encouragement mechanisms such as price signal or feed-in tariffs. Furthermore, this paper investigates and optimizes the effect of μ-level DG units on delivery loss reduction and efficiency improvement of distribution feeders. The paper also proposes two major optimization algorithms for optimum placement and utilization of medium-size and MW-scale DGs in LV distribution networks. Finally, the paper closes with a discussion of policies needed to support efficiency improvement and delivery loss minimization of future distribution systems with large penetration of DG units.

Distributed generation (DG) units are increasing in number all over the world, thus providing new opportunities, but also challenges for distribution system operators. This paper briefly describes the status for integration of DG in Norway, highlighting challenges that Norwegian distribution network companies are facing with regard to large scale integration of DG, typically small scale hydro. The paper further reports from a survey describing some aspects of DG integration and existing planning methodologies among distribution network companies. An overall methodology for planning of integration of DG is presented, highlighting different aspects of inherent uncertainty in the planning process. The methodology can be used by the distribution system operators to develop long term network plans taking into consideration the local potential for DG, but also the uncertainty (in terms of timing, location and capacity) for DG development.

The importance of Distributed Generators (DGs) in the operation of electric power distribution systems both technically and economically has grown significantly in recent times. This is primarily due to the continuous rise in electricity demand which is increasingly challenging to meet with the energy generation. As a result, DGs provide more efficient alternatives for delivering quality energy close to consumption sites. This study modelled a typified Nigerian power distribution network by integrating renewable DG units based on solar Photovoltaics (PV) capacity factors. The study aimed to determine the optimal DG sizing and location using Particle Swarm Optimization (PSO) to enhance network performance. Three optimization scenarios were explored: minimizing total cost with loss, minimizing total cost with voltage stability index and minimizing total cost and loss with voltage stability index. These scenarios considered constraints such as voltage limits, DG power injection, power balance and power flow. The results obtained indicated that the third scenario which focused on minimizing total cost and loss while improving voltage stability yielded the best network performance. It resulted in significant loss reduction, voltage stability improvement and cost savings compared to the other two and the base scenarios.

Albeit possessing their benefits, introduction of extraneous power generation like Distributed Generators (DGs) may cause complications to the existing power system and affect the overall efficiency, reliability and security of the system. Therefore, it is crucial to identify the possible snags and fix them. Additionally, inappropriate placement of DGs in power system may constrain the overall performance of the system. On the other hand, placing a DG accurately would enhance the general operation of a power system tremendously. This paper presents the potential drawbacks that may occur when DGs are added into the power system. Subsequently, a proposed methodology that comprises of power system studies is utilized to determine the optimal location of DGs in a power system. A revised methodology is then suggested which only uses load flow results to determine the best location and is highly efficient compared to the latter. The revised methodology is tested on 6-bus test system which presented identical results as the first methodology but with considerably lesser effort.

Due to the continuous increase of distributed generation (DG) penetration into distribution systems, load flow analysis always needs improvement to be more flexible, fast and computationally efficient especially when integrating DG with existing distribution systems. The work in this paper is divided into two parts; in the first part a new algorithm is developed to solve the power flow problem for radial distribution systems with DG modelled as PV bus. This algorithm can be applied for all distribution systems with any number of buses and laterals. In the second part, the developed load flow programme is employed to study the effect of changing the DG location on electric losses and voltage profile. The best location for DG can be determined accordingly to achieve minimum losses with best voltage profile. Typical case studies are implemented and the results obtained are presented and discussed.

As power system restoration (PSR) is a multi-variable, multi-objective, multi-constrained nonlinear optimization problem, it is difficult to establish mathematical model. Hence, a new distributed model for PSR is proposed in this paper by combing multi-agent technology (MAT) with ant colony optimization algorithm (ACOA). The proposed model consists of a number of bus intelligent agents (BIAGs) and a single intelligent control agent (ICAG). These agents are co-operative and capable to interact with each other dynamically to gain local optimal solutions. Based on that, ACOA is utilized by ICAG to search for a global optimal solution. By the flexible interaction of agents and the heuristic search of ACOA, an optimal target of PSR is obtained. It is shown from simulation results that this model can reach an optimal target of PSR. In addition, the proposed model poses strong robust for the changes of electric network topology and has the flexibility of dealing with various problems during restoration

In the context of sustainable development, electric vehicles (EVs) and renewable-based distributed generation (RDG) integration into the distribution networks (DNs) introduce various merits. They involve lowering harmful emissions, employing various types of energy sources, and encouraging the dependence of renewable energy. However, the two most challenging issues posing a grave danger to the technical operation of the DN would be the inappropriate integration of RDGs and EVs. Consequently, in order to guarantee safe operation, distribution system operator (DSO) is responsible for precisely identifying two crucial terms, distributed generation hosting capacity (DG-HC) and electric vehicles hosting capacity (EV-HC). Despite an increase in the amount of research on HC approaches, there is still a remarkable research gap in the discussion of models that effectively combine demand response (DR), smart inverters (SI) Volt/VAR control, DG-HC, and EV-HC targets simultaneously. This study offers a hierarchical bi-level optimization HC framework depends on both dynamic tariff-based DR and SI Volt/VAR control. In the lower layer, Participating customers’ load curves and EV aggregators’ charging demands are optimally adjusted, on a forecast basis, based on the proposed dynamic tariff. Nonetheless, the suggested multi-objective function—which includes DG-HC, EV-HC maximization, and loss minimization—is optimized by the DSO at the upper layer based on all of these optimal load curves. To further support the proposed objective function, DR is mixed with the optimal Volt/VAR controlling offered by SIs. In addition, the role of grid-connected EVs (GCEVs) on DG-HC increase is revealed with both uncoordinated and coordinated charging schemes. To check the proposed approach robustness, three types of loads are considered. Through comparison with three other optimization approaches, the effectiveness of the equilibrium optimizer (EO) is demonstrated when it is employed to solve the proposed optimization scheme. The suggested planning approach is applied on both the IEEE 33-bus test system, and a real DN with 59 buses in Cairo, Egypt. Several significant conclusions are validated by the obtained results. First, the DG-HC assessment differs significantly depending on whether the EV charging demand is considered or not. Considering the EV integration in IEEE 33-bus, the mean value of the optimal DG-HC increased by more than 133% during the day. Secondly, the implementation of the proposed dynamic-pricing DR program in the IEEE 33-bus DN significantly improved both the DG-HC and EV-HC, named as the combined DG-EV-HC, with improvements of around 34% and 27% for DG-HC and EV-HC respectively. Finally, in IEEE 33-bus, the combined DG-EV hosting capacity was improved by approximately 49.2% regarding DG-HC and 61.2% regarding EV-HC, using the proposed synergistic DR-Volt/VAR control enhancement technique.