Small-scale Distributed Energy Resources Research Articles

A tri-level programming-based frequency regulation market equilibrium under cyber attacks

Owing to their flexibility and rapid response, grid-connected distributed energy resources (DERs) are wielding growing influence in frequency regulation markets (FRMs). Nevertheless, compared with conventional large-scale generators, small-scale DERs are usually weakly shielded by cyber security measures. This offers attackers the opportunity of disrupting the clearing and settlement of FRMs by manipulating the bid information of DERs. In this paper, the frequency regulation market equilibrium is studied considering the dynamic gaming between attackers and defenders, both of which need the knowledge of FRMs for their interventions. Specifically, a tri-level programming model characterizing the attacker–defender–operator (ADO) interdiction problem in FRMs is developed and then analyzed using a column and constraint generation algorithm, thereby achieving market equilibrium representing the defender's best response to the attacker. The defense strategy in the market equilibrium can attenuate the negative influence of cyber attacks upon the FRMs to the maximum extent. Finally, based on the operating rules of the California Independent System Operator, the FRM process considering the ADO interdiction is simulated and the numerical equilibrium results are presented.

DSO와 전기차 충전소의 협조운영 환경에서 스마트 충전 기반 배전계통 전압 관리 방법

The independent operation of small-scale distributed energy resources that do not consider the distribution system causes stability problems such as overvoltage. As one of the methods to solve this problem, a curtailment-based cooperative operation methodology between distribution system operators (DSO) and third parties is being adopted. An economical and efficient distribution system management method is needed because the curtailment method can cause the potential generation of energy waste and economic loss. This study proposes +DR (Demand response) program-based overvoltage management methodology through charging load adjustment of electric vehicle charging stations (EVCS) in a cooperative operating environment. To this end, an AC OPF (Optimal power flow)-based +DR capacity calculation algorithm and an EVCS operator

Distribution Locational Marginal Pricing Under Uncertainty Considering Coordination of Distribution and Wholesale Markets

A rapidly growing amount of small-scale distributed energy resources (DERs) integrated into distribution systems call for an effective distribution electricity market to manage the uncertainty of DERs and remove barriers to the participation of DERs in wholesale electricity markets. To this end, this paper proposes an uncertainty-aware distribution locational marginal pricing (DLMP) mechanism based on robust optimization for day-ahead distribution markets within a transmission-distribution coordinated framework. The transmission-level model clears the wholesale market and forms transmission locational marginal prices (LMPs) to price energy, reserve, and uncertainty. At the distribution level, a robust optimization-based DLMP mechanism is proposed that internalizes uncertainties and coordinates with the wholesale market. Besides active and reactive power DLMPs, the uncertainty DLMP is introduced to reward reserve and charge uncertainties. The novel DLMP mechanism provides transparent and comprehensive price signals for managing voltage, congestion, loss, especially uncertainty. The coordinated model is solved in a decentralized manner by heterogeneous decomposition algorithm. Distribution and wholesale markets are integrated through the coordinated mechanism to fully utilize generation resources. Accordingly, DMLPs are highly correlated and consistent with transmission LMPs, and thus allow DERs to participate in wholesale markets. The effectiveness of the proposed method is verified via numerous case studies.

A decentralized stochastic bidding strategy for aggregators of prosumers in electricity reserve markets

Recent years have seen an increasing interest in demand response as a means to provide flexibility and support the penetration of renewable resources in the power system. Aggregators play a key role, in this regard, by enabling the participation of small-scale distributed energy resources and loads connected at the distribution network into local and central markets.This paper proposes an optimal strategy to support aggregators of a large number of small prosumers to participate in the tertiary reserve procurement market. In particular, a two-stage decentralized approach is proposed in which, first, prosumers individually optimize their flexible appliances schedule according to their own requirements and preferences, without sharing any information with the aggregator. At the aggregator level, a trading strategy is proposed for participation in the reserve market aiming at maximizing the aggregator’s profit under the pay-as-bid pricing scheme, while only requiring a linear programming formulation ensuring the efficiency of the trading problem. In the developed approach, a scenario-based stochastic programming method is introduced to capture the uncertainties of market prices, weather conditions, loads, and user behavior.To demonstrate the applicability of the proposed method, using real data, simulations on 2500 small prosumers in a realistic setting are carried out. The proposed linear and decentralized method can be executed within a few minutes, enabling its applicability for aggregators with a large number of customers.

Aperiodic two-layer energy management system for community microgrids based on blockchain strategy

Regulatory changes in different countries regarding self-consumption and growing public concern about the environment are encouraging the establishment of community microgrids. These community microgrids integrate a large number of small-scale distributed energy resources and offers a solution to enhance power system reliability and resilience. This work proposes a geographically-based split of the community microgrids into clusters of members that tend to have similar consumption and generation profiles, mimicking the most typical layout of cities. Assuming a community microgrid divided into clusters, a two-layer architecture is developed to facilitate the greater penetration of distributed energy resources in an efficient way. The first layer, referred as the market layer, is responsible for creating local energy markets with the aim of maximising the economic benefits for community microgrid members. The second layer is responsible for the network reconfiguration, which is based on the energy balance within each cluster. This layer complies with the IEC 61850 communication standard, in order to control commercial sectionalizing and tie switches. This allows the community microgrid network to be reconfigured to minimise energy exchanges with the main grid, without requiring interaction with the distributed system operator. To implement this two-layer energy management strategy, an aperiodic market approach based on Blockchain technology, and the additional functionality offered by Smart Contracts is adopted. This embraces the concept of energy communities since it decentralizes the control and eliminates intermediaries. The use of aperiodic control techniques helps to overcome the challenges of using Blockchain technology in terms of storage, computational requirements and member privacy. The scalability and modularity of the Smart Contract-based system allow each cluster of members to be designed by tailoring the system to their specific needs. The implementation of this strategy is based on low-cost off-the-shelf devices, such as Raspberry Pi 4 Model B boards, which operate as Blockchain nodes of community microgrid members. Finally, the strategy has been validated by emulating two use cases based on the IEEE 123-node system network model highlighting the benefits of the proposal.

An IGDT-Based Day-Ahead Co-Optimization of Energy and Reserve in a VPP Considering Multiple Uncertainties

The increasing of grid-connected variable renewable energy (VRE) on the demand side causes balance problems in the power system. Thus, dealing with the uncertainty, variability, and consequently, flexibility requirement is becoming an urgent challenge to the power system operators. Virtual power plant (VPP), which bundles different types of small-scale distributed energy resources (DERs) into a single unit for optimization will effectively mitigate those uncertainties. An optimal VPP energy management method is proposed in this article for optimal energy and operating reserve (OR) scheduling. The studied VPP is a cluster of dispersed generating units (including dispatchable and stochastic power sources), flexible loads, as well as storage units. VPP operator has to make decisions based on the uncertainty coming from the stochastic VRE, load demand, as well as market electricity price. Thus, a dynamic risk reserve quantification method is proposed to cover both VRE power and load forecast uncertainties, while information gap decision theory is applied in the unit commitment procedures to study the impact of price uncertainty on the decision-making of VPP operators. Finally, the proposed method is implemented and verified with a case study, and optimal decisions are discussed.

A Real-Time Monitoring Platform for Distributed Energy Resources in a Microgrid—Pilot Study in Oman

This article presents the development of a platform for real-time monitoring of multi-microgrids. A small-scale platform has been developed and implemented as a prototype, which takes data from various types of devices located at a distance from each other. The monitoring platform is interoperable, as it allows several protocols to coexist. While the developed prototype is tested on small-scale distributed energy resources (DERs), it is done in a way to extend the concept for monitoring several microgrids in real scales. Monitoring strategies were developed for DERs by making a customized two-way communication channel between the microgrids and the monitoring center using a long-range bridged wireless local area network (WLAN). In addition, an informative and easy-to-use software dashboard was developed. The dashboard shows real-time information and measurements from the DERs—providing the user with a holistic view of the status of the DERs. The proposed system is scalable, modular, facilitates the interoperability of various types of inverters, and communicates data over a secure communication channel. All these features along with its relatively low cost make the developed real-time monitoring platform very useful for online monitoring of smart microgrids.

Reinventing the utility for distributed energy resources: A proposal for retail electricity markets

In this paper we propose a distribution-level retail electricity market operated by a Distribution System Operator (DSO), to permit participation for small-scale distributed energy resources (DERs) in a real-time energy market. The retail market is built upon a distributed Proximal Atomic Coordination algorithm, which solves the optimal power flow using the convex nonlinear Branch Flow model, rendering spatially and temporally varying distribution-level Locational Marginal Prices. A numerical study of the market structure is carried out via simulations of the IEEE-123 node network using data from ISO-NE and Eversource in Massachusetts, US. The market performance is compared to existing retail practices, including demand response with no-export rules and net metering. Results show the DSO-centric market increases DER utilization, resulting in lower electricity rates for customers, from $0.114/kWh to $0.0291/kWh. Further, we discuss the policy implications of such a market, including DER participation models at the wholesale level in light of FERC Order 2222, and the evolving business model of the modern utility which is moving from commoditized markets towards performance-based ratemaking.

Scalable coordinated management of peer-to-peer energy trading: A multi-cluster deep reinforcement learning approach

The increasing penetration of small-scale distributed energy resources (DER) has the potential to support cost-efficient energy balancing in emerging electricity systems, but is also fundamentally affecting the conventional operation paradigm of the latter. In this context, innovative market mechanisms need to be devised to better coordinate and provide incentives for DER to utilize their flexibility. Peer-to-Peer (P2P) energy trading has emerged as an alternative approach to facilitate direct trading between consumers and prosumers interacting in an energy collective and fosters more efficient local demand–supply balancing. While previous research has primarily focused on the technical and economic benefits of P2P trading, little effort has been made towards the incorporation of prosumers’ heterogeneous characteristics in the P2P trading problem. Here, we address this research gap by classifying the participating prosumers into multiple clusters with regard to their portfolio of DER, and analyzing their trading decisions in a simulated P2P trading platform. The latter employs the mid-market rate (MMR) local pricing mechanism to enable energy trading among prosumers and penalizes the contribution to the system demand peak of each prosumer. We formulate the P2P trading problem as a multi-agent coordination problem and propose a novel multi-agent deep reinforcement learning (MADRL) method to address it. The proposed method is founded on the combination of the multi-agent deep deterministic policy gradient (MADDPG) algorithm and the technique of parameter sharing (PS), which not only enables accelerating the training speed by sharing experiences and learned policies between all agents in each cluster, but also sustains the policies’ diversity between multiple clusters. To address the non-stationarity and computational complexity of MADRL as well as persevering the privacy of prosumers, the P2P trading platform acts as a trusted third party which augments the market collective trading information to help training of prosumer agents. Experiments with a large-scale real-world data-set involving 300 residential households demonstrate that the proposed MADRL method exhibits a strong generalization capability in the test data-set and outperforms the state-of-the-art MADRL methods with regard to the system operation cost, demand peak as well as computational time.

YYPO-PSO Algorithm Based Power Quality Control in a Grid-Connected Hybrid System

Micro-grid is a potential solution for the integration and management of renewable energy generation. Power generation through renewable energy sources has become more preferential and cost-effective, by articulating small scale distributed energy resources, micro-grids are becoming an alternative method in electrical power generation at the distribution voltage level. The increment of nonlinear loads and power electronic interfaced distribution generation system (DG) in the grid creates power quality issues like harmonic distortions in the distributed power system [6] [18]. The microgrid proposed in this paper consists of a photovoltaic array which represents the main generation unit and a three-phase induction generator that supplements the variable power generated by the photovoltaic array; a battery bank is included in the micro-grid to reduce the burden of the power generated by the micro-grid during the peak period [19] [20]. The power electronic interface maintains the necessary adaptability, security and reliability of operation between renewable sources and the distribution system [18]. In this paper, a comprehensive survey on micro-grid to improve the power quality parameters like THD is taken as the main objective with the help of suitable hybrid optimization algorithm, harmonic filters, controllers and battery storage.

Distribution Market-Clearing and Pricing Considering Coordination of DSOs and ISO: An EPEC Approach

Distribution-level electricity market provides a platform for trading energy and grid services from large-amount of small-scale distributed energy resources (DERs) located on distribution grids. The behavior of the DERs in the distribution electricity market may ultimately impact the market-clearing and locational marginal prices (LMPs) in the wholesale market. This paper proposes a bi-level optimization model for distribution market clearing and distribution locational marginal pricing (DLMP) considering the interactions between distribution and transmission wholesale markets. In the proposed model, the upper-level model represents the distribution system operator (DSO) market-clearing and the lower-level model represents the wholesale market-clearing by the independent system operator (ISO). The LMP at the substation will impact the DER dispatch and power demands of the DSO as well as the DLMP. In turn, the power demands of the DSO will further impact the ISO market and its LMPs. The equilibrium problem with equilibrium constraints (EPEC) approach is applied to find the equilibria of multiple DSOs and the ISO. The EPEC problem is transformed into a single-level mixed-integer convex problem in order to allow for efficient solving. The effectiveness of the proposed model and solution method are demonstrated through case studies.

A distribution network planning model considering neighborhood energy trading

The widespread adoption of small-scale distributed energy resources (DERs) amongst energy users has drastically changed the operation of distribution networks. To date, there has not been a consolidated model to incorporate the investment decisions of the end-users in the distribution network planning. The contribution of this paper is a distribution network planning model for the utility which considers the neighborhood energy trading (NET) as a platform for end-users to directly exchange energy between them. The proposed mixed-integer second-order cone programming (MISOCP) problem provides the optimal decisions for line and transformer upgrades, as well as for photovoltaic (PV) and battery in end-users’ premises. Moreover, it indicates a fair allocation of network charges among the participants to NET schemes. The simulation results on the IEEE-33 bus test system confirm the effectiveness of the proposed model in lowering the total cost of the planning and the operation. This platform can be used by government-owned utilities as a guide to avoid sunk investments while motivating the increased installation of renewable distributed generation and storage units by end-users.

Exploring the effects of local energy markets on electricity retailers and customers

Local energy markets (LEM) have recently attracted great interest as they enable effective coordination of small-scale distributed energy resources (DER) at the customer side, and avoidance of distribution network reinforcements. However, the introduction of LEM has also significant implications on the strategic interactions between the customers and incumbent electricity retailers. This paper explores for the first time these interactions by proposing a novel multi-period bi-level optimization model, which captures the pricing decisions of a strategic retailer in the upper level (UL) and the response of both independent customers and the LEM (both including flexible consumers, micro-generators and energy storages) in the lower level (LL). Since the LL problem representing the LEM is non-convex, a new analytical approach is employed for solving the developed bi-level problem. The examined case studies demonstrate that the introduction of an LEM reduces the customers’ energy dependency on the retailer and limits the retailer’s strategic potential of exploiting the customers through large differentials between buy and sell prices. As a result, the profit of the retailer is significantly reduced while the customers, primarily the LEM participants and to a lower extent non-participating customers, achieve significant economic benefits.

Exploiting OLTC and BESS Operation Coordinated with Active Network Management in LV Networks

The large number of small scale Distributed Energy Resources (DER) such as Electric Vehicles (EVs), rooftop photovoltaic installations and Battery Energy Storage Systems (BESS), installed along distribution networks, poses several challenges related to power quality, efficiency, and reliability. Concurrently, the connection of DER may provide substantial flexibility to the operation of distribution grids and market players such as aggregators. This paper proposes an optimization framework for the energy management and scheduling of operation for Low Voltage (LV) networks assuring both admissible voltage magnitudes and minimized line congestion and voltage unbalances. The proposed tool allows the utilization and coordination of On-Load Tap Changer (OLTC) distribution transformers, BESS, and flexibilities provided by DER. The methodology is framed with a multi-objective three phase unbalanced multi-period AC Optimal Power Flow (MACOPF) solved as a nonlinear optimization problem. The performance of the resulting control scheme is validated on a LV distribution network through multiple case scenarios with high microgeneration and EV integration. The usefulness of the proposed scheme is additionally demonstrated by deriving the most efficient placement and sizing BESS solution based on yearly synthetic load and generation data-set. A techno-economical analysis is also conducted to identify optimal coordination among assets and DER for several objectives.

An Efficient Micro Grid Optimization Theory

A Micro Grid is an aggregate of many small-scale distributed energy resources (DERs); loads and can be operated independently or together with the existing power grid as a local power grid. The operator of such a grid takes charge of the energy supply and consumption of these resources and loads available in the grid. Meanwhile, the system operator of the grid considers the entire Micro Grid system to be a single load or a generator and assigns the responsibility of its internal management to the operator. The power production from a passive production resource is largely influenced by external environmental factors such as weather conditions, rather than operating conditions. Thus, this study conducted simulations for the cases where four kinds of conditional expressions had not been applied at all or one of them had been applied to compare and evaluate the effectiveness of each expression. As a result, the conditional equations were found to be effective when attempting to optimize the Micro Grids efficiently.

Review on Power Quality Enhancement and its effects on Micro Grid

Power generation through the renewable energy sources has become more viable and economical than the fossil fuel based power plants. By integrating small scale distributed energy resources, microgrids are being introduced as an alternative approach in generating electrical power at distribution voltage level. The power electronic interface provides the necessary flexibility, security and reliability of operation between micro-sources and the distribution system. The presence of non-linear and the unbalanced loads in the distribution system causes power quality issues in the Microgrid system. This paper explores and reviews different control strategies developed in the literature for the power quality enhancement in microgrids.

Bilateral Contract Networks for Peer-to-Peer Energy Trading

This paper proposes bilateral contract networks as a new scalable market design for peer-to-peer energy trading. Coordinating small-scale distributed energy resources to shape overall demand could offer significant value to power systems, by alleviating the need for investments in upstream generation and transmission infrastructure, increasing network efficiency and increasing energy security. However, incentivising coordination between the owners of large-scale and small-scale energy resources at different levels of the power system remains an unsolved challenge. This paper introduces real-time and forward markets, consisting of energy contracts offered between generators with fuel-based sources, suppliers acting as intermediaries and consumers with inflexible loads, time-coupled flexible loads and/or renewable sources. For each type of agent, utility-maximising preferences for real-time contracts and forward contracts are derived. It is shown that these preferences satisfy full substitutability conditions essential for establishing the existence of a stable outcome—an agreed network of contracts specifying energy trades and prices, which agents do not wish to mutually deviate from. Important characteristics of energy trading are incorporated, including upstream–downstream energy balance and forward market uncertainty. Full substitutability ensures a distributed price-adjustment process can be used, which only requires local agent decisions and agent-to-agent communication between trading partners.

Optimal Operating Schedule for Energy Storage System: Focusing on Efficient Energy Management for Microgrid

A microgrid is a group of many small-scale distributed energy resources, such as solar/wind energy sources, diesel generators, energy storage units, and electric loads. As a small-scale power grid, it can be operated independently or within an existing power grid(s). The microgrid energy management system is a system that controls these components to achieve optimized operation in terms of price by reducing costs and maximizing efficiency in energy consumption. A post-Industry-4.0 consumer requires an optimal design and control of energy storage based on a demand forecast, using big data to stably supply clean, new, and renewable energy when necessary while maintaining a consistent level of quality. Thus, this study focused on software technology through which an optimized operation schedule for energy storage in a microgrid is derived. This energy storage operation schedule minimizes the costs involved in electricity use. For this, an optimization technique is used that sets an objective function representing the information and costs pertaining to electricity use, while minimizing its value by using Mixed Integer Linear Programming or a Genetic Algorithm. The main feature of the software is that an optimal operation schedule derivation function has been implemented with MATLAB for the following circumstances: when the basic operation rules are applied, when operating with another grid, when the external operating conditions are applied, and when the internal operating conditions are applied.

A multi-stage linearized interactive operation model of smart distribution grid with residential microgrids

This paper addresses an interactive energy scheduling model developed between distribution system and residential microgrids (RMGs). To make such concepts, a three-stage mathematical programming framework is proposed. Residential energy management (REM) approach which is in interaction with residential microgrid operator (RMGO) is developed which contains two stages, sequentially. The main goal of these stages is to modify RMGs day-ahead load profile by considering the minimized total daily energy expenses of each home at each RMG. Moreover, in these stages, in addition to existing fixed-loads at each evaluated residential homes, in-home energy management (iHEM) systems are responsible for adjusting the shiftable appliances and small scale distributed energy resources (DERs) commitment. In third stage, besides the interactions between distribution system operator (DSO) and RMG operators (RMGOs), optimal operation cost of the distribution system is determined as well. In this way, optimal scheduling of distribution system active elements namely large scale DERs are considered and the changing trends in energy exchanges, power losses, and voltage profile are addressed. To lower the computational burden of the proposed model, linearization techniques are applied in the proposed model. Simulation studies are reported on modified IEEE 33-bus distribution test system to assess the performance of the proposed model. Results are discussed in depth.

Peer-to-Peer energy trading in a Microgrid

Peer-to-Peer (P2P) energy trading represents direct energy trading between peers, where energy from small-scale Distributed Energy Resources (DERs) in dwellings, offices, factories, etc, is traded among local energy prosumers and consumers. A hierarchical system architecture model was proposed to identify and categorize the key elements and technologies involved in P2P energy trading. A P2P energy trading platform was designed and P2P energy trading was simulated using game theory. Test results in a LV grid-connected Microgrid show that P2P energy trading is able to improve the local balance of energy generation and consumption. Moreover, the increased diversity of generation and load profiles of peers is able to further facilitate the balance.