With the increasing penetration of distributed energy resources (DERs), traditional distribution networks as load-serving entities in wholesale electricity markets, now evolve towards active distribution networks (ADNs) which can proactively participate in wholesale markets by optimally controlling the DERs in their networks. A stochastic bilevel optimization model is proposed in this paper for the strategic participation of ADNs and DERs to provide energy and grid services in wholesale electricity markets. The bilevel optimization model can capture the interactions between the ADN and the wholesale energy and ancillary service markets, considering the uncertainties of DERs in the ADN. In the upper-level model, the ADN makes optimal decisions on energy and reserve bidding considering the availability, uncertainties, and flexibility of DERs. The joint energy and reserve market-clearing of the independent system operator (ISO) is modeled as the lower-level problem. Using strong duality theory and Karush-Kuhn Tucker (KKT) conditions, the proposed bilevel optimization problem is reformulated as mathematical programming with equilibrium constraints (MPEC) problem and further converted into a computationally-solvable mixed-integer second-order-cone programming (MISOCP) model. The simulation results demonstrate the effectiveness of the model and the interactions between an ADN and wholesale electricity markets.

Peak-time rebates offer an opportunity to introduce demand response in electricity markets. To implement peak-time rebates, utilities must accurately determine the consumption level if the program were not in effect. Reliable calculations of customer baseline load elude utilities and independent system operators, due to factors that include heterogeneous demands and random variations. Prevailing research is limited for residential markets, which are growing rapidly with the presence of load aggregators and the availability of smart grid systems. Our research pioneers a novel method that clusters customers according to the size and predictability of their demands, substantially improving existing customer baseline calculations and other clustering methods.

Behind-the-meter battery energy storage is used to mitigate non-dispatchability of customer-owned renewable sources. While serving their owners’ objectives, their underutilized capacities can be used to provide grid services to justify their cost. However, the integration of these resources extends the size and complexity of the network operation problem, imposing a processing burden on the central control system. In this paper, an agent-based distributed control framework is proposed that enables utilizing of the underutilized behind-the-meter capacities. In this framework, while the prosumer agents initially schedule their resources for daily cost management, a local customer aggregator agent performs day-ahead active and reactive power control on the underutilized behind-the-meter batteries to provide multiple grid services. A novel grouping algorithm is developed based on voltage sensitivity and a modified $k$-means clustering method to assign customer groups to the local agents and establish the network subsystems. Mixed-integer linear programming is used to model and solve multiple optimization problems. The proposed framework is applied to the 123-bus distribution network. The simulation results show its effectiveness in terms of complexity and optimality, compared to the central control approach in allocating the unused behind-the-meter resources for two common industry needs: network bottleneck congestion relief and grid-edge voltage support.

This article proposes a framework as a remedial action scheme (RAS) for close to real-time system operation when system operator becomes aware of possibility of decentralized outages due to simultaneous attacks on several market retailers. The upper level of the framework is an offline RAS where system operator immediately implements a customized reconfiguration preventing the decentralized outages. Due to immediate implementation of upper-level reaction, it is possible to face decentralized congestions in the new topology having minimum reliance on the attacked market retailers. Since severity of congestions is less than that of outages, the lower level of the framework is implemented as an online RAS. In this level, eligible nonattacked market retailers are invited into a secondary market to compete for generation/consumption redispatch and contribute into alleviation of congestions with the purpose of increasing their income. The effectiveness of the proposed framework is validated on a well-known 136-bus distribution system.