Portfolio Of Distributed Energy Resources Research Articles

Participation of Aggregated DERs to the Ancillary Services Market: A Monte Carlo Simulation-Based Heuristic Greedy-Indexing Model

In an effort to improve the stability and secure operation of the grid, regulatory bodies are opening Ancillary Services Markets participation to Distributed Energy Resources (DERs), energy storage systems, and demand response. Within this framework, this study proposes a model that simulates the coordinated operation of an aggregate of power plants, including non-dispatchable DERs and, as regulating units, Combined Heat and Power (CHP) generation and electrochemical energy storage systems. A Monte Carlo procedure is adopted to realistically create a population of aggregation scenarios. The real-time operation of the DER portfolio is managed through a Heuristic Greedy-Indexing logic, which allows the Aggregator to select the optimal control action to implement according to the technical and economic quantities characterizing the market and the grid. The techno-economic performance of the proposed algorithm is evaluated by simulating its interaction with the electricity markets. Finally, a sensitivity analysis is performed to analyze the profitability in different scenarios. The novel mathematical model proposed showed to be effective in managing a complex problem like the one at hand with an acceptable computational effort. The numerical results obtained confirmed that the aggregated participation in the market could provide interesting economic returns, especially if a CHP unit is involved as regulating unit, while the feasibility of the batteries adoption is still limited by the actual cost of the technology.

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.

Integrated techno-economic modeling, flexibility analysis, and business case assessment of an urban virtual power plant with multi-market co-optimization

The concept of Virtual Power Plant (VPP) is recognized as an effective option to aggregate and operate Distributed Energy Resources (DER) to participate in wholesale energy markets and provide flexibility and associated grid services that are needed in a renewable-rich energy system. Also, as most of the DER are available in urban areas, there are increasing interests in assessing the potential to develop urban VPP, for example in university campuses. However, exploiting the flexibility of VPP and developing robust business cases require advanced considerations on their technical and commercial constraints and trade-offs in deploying the VPP’s flexibility when simultaneously participating in multiple markets. In this context, this paper presents a comprehensive, integrated techno-economic modeling approach that assesses the technical and commercial flexibility opportunities and develops a relevant business case framework based on co-optimized participation in multiple markets for an urban VPP. A real-world case study based on the University of Melbourne’s new campus under development is used to demonstrate the proposed approach, including the VPP’s participation in the energy, frequency control ancillary services, demand response, and hedging contract markets. The technical analysis shows that diversity of DER portfolio results in improved participation of VPP in various markets. From an economic perspective, a multi-market co-optimization model such as the one proposed here, fully exploiting the DER’s aggregated flexibility, results in attractive business cases for operating DER in urban areas as a VPP. The proposed approach and examples provided may be seen as a blueprint for more VPP applications and unlocking the great flexibility available in urban areas.

Optimal renewable generation and battery storage sizing and siting considering local transformer limits

In response to climate change and sustainability challenges, various incentive programs have increased solar photovoltaic (PV) generation interconnection in the low voltage electrical distribution system. However, electric utility support of PV generation is limited by reverse power flow into the electricity network at high penetration levels. This limits the ability to achieve Zero Net Energy (ZNE) behind individual meters and in whole communities. In parallel, district level energy systems, and Advanced Energy Communities (AEC) that include storage, offer a great prospect for integrating high levels of Distributed Energy Resources (DER) into the built environment. Optimally designing such systems to serve communities, commercial, and industrial loads, while maximizing the penetration of solar PV, has been a challenge to Distribution System Operators (DSO) and city planners. This paper proposes a Mixed Integer Linear Program (MILP) optimization to decide the best DER portfolio, allocation, and dispatch, for an AEC that achieves ZNE and islanding while respecting electrical grid operational constraints, with a focus on distribution transformer overloads. The main strategies to avoid transformer overloads were found to be judicious sizing and siting of battery energy storage and also optimally re-distributing PV throughout the community, which increased the ability of the electric infrastructure to support a PV deployment that is 1.7 times larger than the existing transformer capacity without the need for infrastructure upgrades. This work highlights the importance of including local infrastructure capacities, such as distribution transformer constraints when developing projects that result in high renewable penetration throughout the distribution network.

Investigating the impact of distributed energy resources on market power of strategic utility corporation

Market participants may employ potential market power improperly in energy trading. On the other hand, integrations of distributed energy resources (DER) are highly complex since it entails the optimal coordination of a diverse portfolio of DER under multiple sources of uncertainty. A large number of possible stochastic realisations that arise can lead to complex operational models that become problematic in real-time market environments. Although recent works have explored the impacts of DER on numerous aspects of grid operation and planning, its role in imperfect competitive energy markets has not been investigated nonetheless. This article proposes the theoretical and quantitative analysis of the withholding strategies for the utility corporations with the integration of DERs for the first time and the corresponding market power effects on utility corporation's profits and market prices. The quantitative demonstration is supported by a bi-level model with the optimal company profit for the upper level and the market clearing for the lower level. This bi-level problem can be solved directly when a single-level problem is obtained with a mathematical program with equilibrium constraints (MPEC). Numerical studies are implemented on a wholesale market with the day-ahead horizon and hourly resolution.

Valuation of distributed energy resources in active distribution networks

In concert with the transformation of conventional passive power distribution system, distributed energy resources (DERs) have progressively become participants in the provision of electricity services in active distribution networks (ADNs). In this paper, we propose a systematic valuation process to quantify the value of DERs in the ADN context. The paper first provides comprehensive insights into the impacts of DERs on ADN and the society as a whole. Given the technological, locational, and temporal diversity of DERs, a two-part scheme is developed to value and compensate DER portfolios proposed by customers and independent third parties. In particular, DERs are valued for their benefits and costs in both short and long terms. An integrated resource planning model is formulated to quantify the value of a given DER portfolio to be installed, where bi-level optimization techniques are applied to coordinate decisions on ADN planning and operations. In order to determine the short-term operation benefits of the DER portfolio on a continuous basis, a retail market operation model is developed based on peer-to-peer energy transactions among prosumers, when the impacts of DERs on ADN operations are monetized by distribution locational marginal prices. It is finally concluded in the paper that the proposed valuation scheme will not only contribute to the proactive investment of DERs in ADN but also help enhance the role of DERs in offering affordable, reliable, resilient and sustainable electricity services to customers.

Stochastic Dual Dynamic Programming for Operation of DER Aggregators Under Multi-Dimensional Uncertainty

The operation of aggregators of distributed energy resources (DER) is highly complex, since it entails the optimal coordination of a diverse portfolio of DER under multiple sources of uncertainty. The large number of possible stochastic realizations that arise can lead to complex operational models that become problematic in real-time market environments. Previous stochastic programming approaches resort to two-stage uncertainty models and scenario reduction techniques to preserve the tractability of the problem. However, two-stage models cannot fully capture the evolution of uncertain processes and the a priori scenario selection can lead to suboptimal decisions. In this context, this paper develops a novel stochastic dual dynamic programming approach which does not require discretization of either the state space or the uncertain variables and can be efficiently applied to a multi-stage uncertainty model. Temporal dependencies of the uncertain variables as well as dependencies among different uncertain variables can be captured through the integration of any linear multidimensional stochastic model, and it is showcased for a p-order vector autoregressive model. The proposed approach is compared against a traditional scenario-tree-based approach through a Monte-Carlo validation process, and is demonstrated to achieve a better trade-off between solution efficiency and computational effort.

A Voltage Controlled PV Inverter for a Certs Microgrid Applications

The Photovoltaic (PV) sources are more compatible for Microgrids because of their ability to internally aggregate and balance with other renewable sources. The conventional grid connected PV inverters are basically current source controlled so unable to control ac voltage or frequency. The PV inverter using the Consortium for Electric Reliability Technology Solutions (CERTS) concepts can control ac voltage and frequency but have a major problem with load transients. The over loaded PV micro source causes dc bus voltage collapse and finally resulting in an ac voltage drop during the load transients. A photovoltaic inverter is capable of operating in both island and grid-connected mode by means of a reconfigurable control scheme. This paper presents an efficient control strategy which causes the PV inverter to act as voltage source to maintain stable dc bus voltage under load transients. With this PV inverter control configuration, it is shown that the PV micro source can operate as a voltage source in the CERTS microgrid. Evolutionary changes in the regulatory and operational climate of traditional electric utilities and the emergence of smaller generating systems such as micro sources have opened new opportunities for on-site power generation by electricity users. In this context, distributed energy resources (DER) - small power generators typically located at users' sites where the energy they generate is used - have emerged as a promising option to meet growing customer needs for electric power with an emphasis on reliability and power quality. The portfolio of DER includes generators, energy storage, load control, and, for certain classes of systems, advanced power electronic interfaces between the generators and the bulk power provider. This paper proposes that the significant potential of smaller DER to meet customers' and utilities' needs can be best captured by organizing these resources into Micro Grids. The Consortium for Electric Reliability Technology Solutions (CERTS) MicroGrid concept assumes an aggregation of loads and micro sources operating as a single system providing both power and heat. The majority of the micro sources must be power electronic based to provide the required flexibility to insure operation as a single aggregated system. This control flexibility allows the CERTS MicroGrid to present itself to the bulk power system as a single controlled unit that meets local needs for reliability and security. The CERTS Micro Grid represents an entirely new approach to integrating DER. Traditional approaches for integrating DER focus on the impacts on grid performance of