Distributed Generation Owners Research Articles

A risk-averse approach for distribution locational marginal price calculation in electrical distribution networks

As distributed generation (DG) systems continue reshaping power networks, optimizing pricing strategies at DG buses using the distribution locational marginal price (DLMP) concept becomes critical for economic efficiency and the optimal operation of the network. This paper proposes a novel approach that combines Information Gap Decision Theory (IGDT) and Particle Swarm Optimization (PSO) to address these challenges. IGDT offers a robust framework for optimizing pricing in the presence of uncertainties and incomplete information in the distribution network. It quantifies and models these uncertainties, enabling the creation of adaptable pricing strategies that respond effectively to changes in DG output and market conditions. To further refine pricing strategies, PSO is integrated into the IGDT framework. PSO's iterative approach allows for the discovery of optimal pricing parameters at DG buses, striking a balance between maximizing DG owner profits and ensuring system optimality. Extensive simulations on two realistic distribution network models validate the effectiveness of the proposed IGDT-PSO approach. It demonstrates that DG owners can reliably achieve their predefined profit targets while also adhering to the objectives of the distribution network.

How can the dynamic game be integrated into blockchain-based distributed energy resources multi-agent transactions for decision-making?

Distributed energy resources not only change the energy supply modes but also have great potential in energy transformation. There are some problems in distributed energy trading, such as high transaction frequency, small transaction volume, high operation cost, lack of information supervision and so on. The openness, transparency, tamper-proof and automatic execution of smart contracts of blockchain technology are consistent with the transaction demand of distributed energy. Therefore, this paper proposes a multi-agent transaction model of distributed energy based on blockchain. First, the peer to peer transaction mode and transaction process are analysed. Second, a multi-agent dynamic Stackelberg game is launched. It takes distributed generation owners, energy storage companies and a grid corporation as leaders to conduct power bidding. Third, with consumers as followers, the time of use tariff bidding strategy was solved. The obtained results reveal that distributed energy resources are essential in the energy supply of carbon neutrality, which also determines the complementary relationship between a grid corporation and distributed generation owners. The two agents work together to exert effectiveness for the new power system. There is a large space for energy storage companies to improve, and consumers show a trend of off-peak power consumption.

OPTIMIZATION OF DG UNIT SIZE AND PLACEMENT IN RADIAL DISTRIBUTION NETWORKS USING AN SLFO-BPSO HYBRID APPROACH

The addition of distributed generation (DG) to an integrated delivery system has significantly increased the system's performance and efficiency. Researchers have proposed a number of alternative approaches to delivery system planning with DG insertion. In this article, a multiobjective approach is introduced to optimise both the delivery system operator's and the DG owner's reciprocal benefits. The reason behind this multi-objective approach is the inconsistent relationship between DG MVA rating reduction and device power losses reduction. To minimize system active power loss while also reducing DG size, the best compromise size of DGs in MVA, their operating power factors, and positions are obtained. The radial delivery system of IEEE-69 buses is called test system. Using the multi-objective Shuffle Frog Leap Algorithm (SFLA) for optimization, the Pareto-front of nondominated solutions can be found. The SFLA algorithm's efficiency is equivalent to that of the Binary PSO (BPSO) algorithm. The result of the best compromised solution of DGs placement in the delivery system is measured using various system operating indices such as active power loss, reactive power loss, and voltage variance.

A Multi-Objective Hybrid Genetic Algorithm for Sizing and Siting of Renewable Distributed Generation

Renewable generation has been addressed in several aspects but it still represents a new paradigm for the expansion of the electricity supply. This paper aims to propose a new model for the sizing and siting problem of distributed generation (DG), based on renewable sources and considering three main aspects: technical, from the distribution utility viewpoint; economical, from the DG owner’s viewpoint, and environmental, from a sustainability perspective. A multi-objective Genetic Algorithm and the Maximin metric are implemented to obtain optimal Pareto sets; also, three decision criteria, considering the concept of preference, are applied to select a final solution from Pareto sets. Case-studies are carried out in medium voltage systems: the 69-bus distribution test system, known from literature, and a 918-bus Brazilian distribution system. Diversity of alternatives in the obtained Pareto sets testify algorithm effectiveness in searching for solutions to the distributed generation sizing and siting problem, in order to ensure power loss reductions, investment return, and environmental benefits. The proposed methodology contributes to the discussions and perspectives among electricity utilities, DG owners, society, and regulators regarding planning and decision making tools.

Locational marginal price computation in radial distribution system using Self Adaptive Levy Flight based JAYA Algorithm and game theory

Abstract This study presents a computational approach to compute locational marginal price (LMP) at distributed generation (DG) buses in an electric power distribution system using self-adaptive levy flight based JAYA algorithm and proportional nucleolus theory (PNT). This method provides financial incentive to DG owners based on their contribution in reliability improvement, loss and emission reduction. In this study expected energy not supplied (EENS) is used for measuring the reliability of a given radial distribution network. This method is implemented on 38 bus distribution system under MATLAB environment to compute LMP values at each DG as per its contribution towards reliability improvement, loss reduction and emission reduction. It is found from the study that reliability has been improved, losses and emissions of system were reduced by providing proper financial incentives to DG owners. The proposed method can be utilized by a distribution company (DISCO) to operate network optimally and to estimate state of network.

A Novel Energy Trading Framework Using Adapted Blockchain Technology

Microgrid planning philosophies are changing from islanding in the case of abnormal conditions to independent sustainability for constant secure, reliable operation. Large independent operators foresee a shift from gigantic-grid bulk generation and transmission to distributed generation (DG) from smaller, interlinked grid clusters. Customers and the community will benefit, but system operators and utilities will face challenges. Introduced to enhance electricity exchange and the energy market structure in such grids, transactive energy networks favor customers and DG owners, establish a utility business model, and enable power system innovation. Planners are also increasingly interested in blockchains for secure transactions. Blockchain-based transactive energy markets promise flexibility, transparency, security, competition, and superlative low-cost reliability, offering ideal energy-trading solutions in isolated microgrids and distribution-level markets. This article presents the development and case-study validation of a comprehensive transactive energy market framework with linked blockchain and power system layers, a novel market structure based on an end-user marginal price, and an adapted blockchain that fits intrinsic power system requirements. A new slot-ahead electricity market model is established through integration with the modified blockchain. With blockchain operation, monetary funds are managed equitably so that wallet billing rates for customers, utilities, and DG owners match broadcast smart-meter data.

A branch oriented active power loss allocation method for radial distribution networks with distributed generators

The penetration of distributed generation (DG) units in the radial distribution network (RDN) introduces complexity in active power loss allocation (LA) as it leads to reverse current in the network. This current makes power system bidirectional and brings difficulties in the decomposition of cross-terms of power loss equation. To overcome such complications, this paper proposes a new branch oriented LA technique which eliminates the impact of mutual-term mathematically from loss formulation without any assumptions and approximations. It establishes a direct relationship between the subsequent load currents of a branch and its two end voltages in terms of the complex power available at its load ends. The proposed LA scheme is found to be fair as regard to the topology of the RDN. Further, the implementation of DGs may increase/decrease power loss of a system. In order to provide the exact amount of loss reduction benefit to the DG owners (DGOs), a new DG remuneration strategy is developed in this paper which assigns either rewards/penalties to the DGOs after analysing their actual impact towards system loss reduction. The effectiveness of the proposed LA method is investigated against various established LA techniques using two different test systems i.e., 17-bus and 33-bus RDNs.

Heuristic approach for transactive energy management in active distribution systems

The advent of distributed energy resources (DERs), which include small conventional and renewable generation units, energy storage, and flexible loads in distribution network needs distributed coordination for effective management. In this paper, a stochastic transactive management framework is proposed to minimize overall cost and avoid network constraints violation at the distribution network level. This framework includes day-ahead scheduling of electric vehicles and air conditioning loads under demand response aggregators (DRAs), and DERs under distributed generation owners (DGOs) into day-ahead wholesale market in a network managed by a distribution network operator (DNO). An agent called distribution independent system operator (DISO) is responsible for coordination among DRAs, DGOs, and DNO. A heuristic step-size update approach is proposed to calculate the Lagrange multiplier iteratively and improve the convergence speed. This framework is modeled as a quadratic constraint programming (QCP) problem and solved using the GAMS solver. Simulation results on a modified 33-bus system with considerable penetration of loads and DERs, shows that the suggested framework can efficiently reduce the iterations to converge and returns an optimal schedule. And demonstrate the effect of network congestion, demand, and generation uncertainties on the resulting objective values of the agents and magnitude of the Lagrange multiplier values.

Probabilistic locational marginal price computation in radial distribution system based on active power loss reduction

This study presents a probabilistic approach to calculate locational marginal price (LMP) at distributed generation (DG) buses in an electric power distribution system to ensure that the network operates at reduced active power losses (APLs). The proportional nucleolus theory-based iterative method is proposed as a deterministic approach to compute LMP based on APLs. Uncertainties in load and market price were captured by using a 2 m + 1 scheme of the point estimation method. To compute the contribution of the reactive and active power of the DG on the APL of the network, loss sensitivity factors have been developed. The proposed method provides an active power price in terms of LMP and reactive power price to each DG as per its contribution to loss reduction. To verify the performance of the proposed method, it was implemented on the Taiwan Power Company distribution system. The proposed method can be utilised by a distribution company to operate the network at reduced APL and improve the benefit of DG owners.

An analytic hierarchy process-based novel approach for benchmarking the power quality performance of grid-integrated renewable energy systems

The technical, social, economic as well as environmental multi-benefits of renewable energy sources do not come without certain power quality (PQ) challenges, and deterioration of voltage and current quality is foremost among them. Technically, voltage harmonic distortion, current harmonic distortion, voltage and frequency fluctuations and voltage imbalances are the five decisive issues that judge the PQ performance of renewable energy systems. Healthy PQ, in terms of all the aforementioned phenomena, is assured by monitoring and benchmarking their corresponding indices, but performing the same, only at distributed generation (DG) bus level, is not sufficient since also the grid, as well as load bus PQ, plays an important role which makes the overall PQ assessment of renewable energy systems quite complex. This paper presents an analytic hierarchy process-based novel approach for evaluating and benchmarking the PQ performance of grid-integrated renewable energy systems. The procedure results in a single compound power quality index that is alone ample for benchmarking the overall PQ performance of a renewable energy system above the threshold level as well as for ranking among the PQ performances of individual DG systems differentiated by either renewable energy types or sittings in a distribution network. Such a compound index can ease the PQ performance evaluation and benchmarking process up to a maximum extent. Furthermore, it can be very helpful also for distribution system operators to monitor and penalize the DG owners for poorly operating from PQ aspects and motivate the same for adopting best option among different power quality improvement (PQI) technologies. The proposed approach is verified on a grid-integrated PV-based DG system in MATLAB/Simulink. Besides that, the effect of different PQI techniques, on PQ performance, is tested by the application of the proposed approach. At last, the validation of impending effects of high penetration of renewable energy on PQ of the system is also attempted by the proposed approach.

Distributed Optimal Dispatching of Multi-Entity Distribution Network With Demand Response and Edge Computing

With large-scale penetrations of distributed generation (DG) and flexible loads, there will be multiple entities in traditional distribution network, such as distribution network operator (DNO), DG owner, and prosumers. Aiming at the collaborative optimization problem of distribution network among multiple entities, a distributed optimal scheduling approach of distribution network considering demand response and edge computing is proposed in this paper. Firstly, the virtual region decomposition method is proposed to divide the original distribution network into multiple regions according to different entities, and the bi-level optimization framework based on edge computing is constructed. Secondly, the optimal models of DNO, DG owner, and prosumers are established respectively, and the distributed optimal scheduling approach of distribution network with collaboration of control center and edge nodes is proposed. Then, the KKT conditions are adopted to realize the transformation of optimal models of DG owner and prosumers. Finally, the proposed distributed optimization scheduling approach is verified based on the modified IEEE33-node system. The results show that the proposed distributed optimal scheduling method can achieve better collaborative optimization among different entities in distribution network compared with the centralized optimization method.

A Circuit Theory-Based Loss Allocation Method for Active Distribution Systems

The integration of optimally placed distributed generation (DG) units in a distribution system can contribute significantly in loss reduction among distribution feeders. In practice, this loss reduction may provide associated benefit to load points (consumers) though they are not contributing for the same. The loss allocation strategy should be such as to benefit DG owners who have actually contributed toward loss reduction. This paper presents a new circuit theory-based loss allocation method for active distribution systems. The method first employs branch-oriented approach to decompose cross-terms of power loss among contributing load points. Thereafter, the contributing currents of DGs are determined using superposition principle to allocate their remuneration/penalty. The proposed method is applied to 33-bus test distribution system. The application results highlight the importance of the proposed method.

Islanding detection of distributed generation by using multi-gene genetic programming based classifier

This paper proposed a new method for detecting islanding of distributed generation (DG), using Multi-gene Genetic Programming (MGP). Islanding has been a serious concern among power distribution utilities and distributed generation owners, because it poses risks to the safety of utilities’ workers and consumers, and can cause damage to power distribution systems’ equipment. Therefore, a DG must be disconnected as soon as an islanding is detected. In addition, an islanding detection method must have high degree of dependability to correctly discriminate islanding from other events, such as load switching, in order to avoid unnecessary disconnection of the distributed generator. In this context, the novelty of the proposed method is that the MGP is capable of obtaining a set of mathematical and logic functions employed to detect and classify islanding correctly. This is a new approach among the computational intelligent methods proposed for DG islanding detection. The main idea was to use local voltage measurements as input of the method, eliminating the need of complex and expensive communication infrastructure. The method has been trained with several islanding and non-islanding cases, by using a power distribution system comprising five concentrated loads, a synchronous distributed generator and a wind power plant. The results showed that the proposed method was successful in differentiating the islanding events from other disturbances, revealing its great potential to be applied in anti-islanding protection schemes for distributed generation.

A multi objective approach for placement of multiple DGs in the radial distribution system

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.

LMP computation at DG buses in radial distribution system

PurposeThe purpose of this paper is to estimate the locational marginal price (LMP) at each distributed generation (DG) bus based on DG unit contribution in loss reduction. This LMP value can be used by distribution company (DISCO) to control private DG owners and operate network optimally in terms of active power loss.Design/methodology/approachThis paper proposes proportional nucleolus game theory (PNGT)-based iterative method to compute LMP at each DG unit. In this algorithm, PNGT has been used to identify the share of each DG unit in loss reduction. New mathematical modeling has been incorporated in the proposed algorithm to compute incentives being given to each DG owner.FindingsThe findings of this paper are that the LMP and reactive power price values for each DG unit were computed by the proposed method for the first time. Network can be operated with less loss and zero DISCO’s extra benefit, which is essential in deregulated environment. Fair competition has been maintained among private DG owners using the proposed method.Originality/valuePNGT has been used for the first time for computation of LMP in distribution system based on loss reduction. Incentives to each DG unit has have been computed based on financial savings of DISCO due to loss reduction. Share of active and reactive power generation of each DG unit on change in active power loss of network due to that DG unit has been computed with new mathematical modeling. The proposed method provides LMP value to each DG unit in such a way that the network will be operated with less loss.

Exact Cross-Term Decomposition Method for Loss Allocation in Contemporary Distribution Systems

High penetration of distributed generation (DG) units in contemporary distribution systems can contribute significantly towards loss reduction in distribution feeders. The loss allocation method should reward the benefit of loss reduction to DG owners (DGOs), instead of diverting the same to the load points which actually never contribute towards loss reduction. This paper presents an exact cross-term decomposition method that decomposes each crossed term of power loss/remuneration among contributing network users while independently addressing their active and reactive power transactions. Separate allocation factors are proposed to decompose each individual crossed term involved. The allocation factors are not based upon heuristic, as in several existing methods, but are derived analytically. The method employs superposition principle to evaluate contributing currents of DGs. Thereafter, the losses/remunerations are allocated fairly among network users by suggesting a suitable remuneration strategy for DGOs. The proposed method is investigated on standard test distribution system. The application results are promising compared with other established methods.

Branch current decomposition method for loss allocation in contemporary distribution systems

The presence of distributed generation (DG) units in reconfigured distribution systems introduces complexity in loss allocation (LA). DG integration alters feeder power losses so DG owners (DGOs) may be incentivized or penalized accordingly, whereas network reconfiguration alters feeding path of network user’s thus further increases complexity in LA. The LA method should judiciously allocate losses among load points and simultaneously reward DGOs and distribution network operator (DNO) for their contribution towards loss reduction. Moreover, the LA method should take care of the reactive power transactions of network users. This paper proposes a branch current decomposition method (BCDM) based upon circuit theory for allocating losses in active reconfigured distribution system while giving due consideration to reactive power transactions of network users. BCDM considers virtual branch voltage drops which is well supported by analytical treatment. In addition, Superposition is utilized to incentivize/penalize DGOs. A new LA strategy is proposed for fair allocation of loss/loss incentives among network users and DNO. Proposed method is thoroughly investigated under varying loading, load power factors and under varying network topologies. The comparison results obtained on standard test distribution system highlights the importance of proposed method.

Carbon tax, system marginal price and environmental policies on Smart Microgrid operation

PurposeThe purpose of this paper is to optimally operate a Smart Microgrid which is interconnected to the main grid so as to minimize expenditures associated with CO2 emissions. Microgrids could come into play to aid the network through CO2 emission reduction while increasing their efficiency through local generation. For this purpose, a Smart Microgrid incorporating Distributed Energy Resources (DER), especially Renewable Energy Sources (RES), is operated optimally while keeping the CO2 emissions in check in order to minimize the financial burden from emissions stemming from the carbon tax. Since the network is assumed to be interconnected with the main grid, there is a consideration of the expected emissions associated with the imported energy.Design/methodology/approachAn economic/environmental dispatch problem is mathematically formulated using an objective function and the constraints that it is subject to. The methodology is applied on a typical 17-bus test distribution network, representing a Hellenic LV network. Various carbon tax rates and their impact on the system marginal price are examined, in terms of their effect on distributed generation (DG) and as a second step, the effect of imposing lower carbon tax rates for micro-sources with the goal of benefitting from their more eco-friendly generation capabilities. In order to assess that benefit, hourly grid emissions coefficients are derived based on actual grid data.FindingsThe CO2 tax refund policy towards the DG owners can lead to optimal coverage of consumers, optimal financial result both for the DG owners and the operator and greater DG integration within the smart grid.Originality/valueGreater DG integration within the smart grid by using a CO2 tax refund policy.

Hybrid genetic dragonfly algorithm based optimal power flow for computing LMP at DG buses for reliability improvement

This paper proposes an hybrid method to compute locational marginal price at distributed generation (DG) buses in order to improve reliability in radial distribution system (RDS). This method consists of optimal power flow based on hybrid genetic dragonfly algorithm which provides incentives to each DG unit based on its contribution to reliability improvement. In this paper expected energy not supplied has been used as a reliability measuring index. The proposed method enables the distribution company (DISCO) to operate the network with more reliability by providing proper incentives to DG owners. The proposed method has been implemented on 38 bus RDS and pacific gas and electric company (PG&E) 69 bus RDS under MATLAB environment. It has been observed from the analytical study on both test systems that by providing proper incentives to DG units, reliability of the network can be vastly improved.

Power system distribution planning considering reliability and DG owner's profit

A novel comprehensive approach to solve a distribution systems' planning problem and to determine the optimal size, location, and technology of the distributed generation (DG) units with respect to the reliability of the system is presented in this paper. The objective functions of this optimization problem are power losses, DGs installation and operation costs, the DG owner's and the Distribution Company's profits as economic objectives. In the proposed method, new indices are defined to evaluate the reliability of the system. These indices calculate energy not supplied and volt ampere reactive not supplied due to active and reactive power shortages. Also, the average interruption frequency (AIF) and average interruption duration (AID) have been considered be minimized during optimization problem. The last two (AIF and AID) are modeled by the adequacy transition rate of DGs among states and are combined as a new objective function. To handle several objective functions, an adaptive fuzzy interactive multi-objective optimization method is used. The results on the IEEE 34-bus and 69-bus power distribution system showed the efficiency of proposed indices for reliability assessment of distribution system planning.