This paper proposes five market models for the procurement of flexibility by transmission (TSO) and distribution system operators (DSOs), based on several TSO-DSO coordination schemes, including a disjoint distribution, disjoint transmission, common, fragmented, and multi-level market. The properties of these models are then analyzed. In particular, we prove that the common market is more efficient than the other market models. Then, different methods are proposed to adequately price TSO/DSO interface flows, when procuring cross-grid flexibility. We show that when interface flows are optimally priced, the fragmented and multi-level market solutions converge to those of the common market. To prevent the need for any network information sharing in the coordination schemes, decomposition methods based on bi-level programming and the alternating direction method of multipliers (ADMM) are proposed. A developed case study, considering an interconnected transmission-distribution system, corroborates the greater efficiency of the common market, the effect of adequate interface pricing on reducing procurement costs, and the capability of the decomposition methods to reach optimal market solutions with limited information exchange.

Non-cooperative scheduling games can be used to coordinate residential loads in order to achieve a common goal while accounting for individual consumer’s interests, privacy, and autonomy. However, a significant portion of the residential flexibility—Thermostatically Controlled Loads (TCLs) such as water and space heating/cooling appliances—has not been fully addressed under this game theoretic approach: their comfort constraints and integer control were not considered. This paper presents a method for properly including TCLs in this framework and discusses its application in energy communities. Specifically, we propose a general mathematical formulation for considering users’ comfort in non-cooperative games. We model the integer nature of the TCLs control with binary variables and show that optimal or close to optimal (less than 1%) solutions are reached. Moreover, different total cost functions can be used depending on the market context and the objective of the demand management program. To illustrate and discuss these aspects in practical applications, we used a case study of an energy community in Spain. The results show that the TC solutions are optimal or only 0.80% worse than optimal; different total cost functions result in different results (load curve smoothing or peak load reduction); consumers’ comfort is respected; and the proposed game model cooperates with consumers in order to minimize community’s costs.

This paper investigates sequential flexibility markets consisting of a first market layer for distribution system operators (DSOs) to procure local flexibility to resolve their own needs (e.g., congestion management) followed by a second layer, in which the transmission system operator (TSO) procures remaining flexibility forwarded from the distribution system layer as well as flexibility from its own system for providing system services. As the TSO does not necessarily have full knowledge of the distribution grid constraints, this bid forwarding can cause an infeasibility problem for distribution systems, i.e., cleared distribution-level bids in the TSO layer might not satisfy local network constraints. To address this challenge, we formally introduce and examine three methods aiming to enable the grid-safe use of distribution-located resources in markets for system services, namely: a corrective three-layer market scheme, a bid prequalification/filtering method, and a novel bid aggregation method. Technically, we provide conditions under which these methods can produce a grid-safe use of distributed flexibility. We also characterize the efficiency of the market outcome under these methods and reflect on their practicalities. Finally, we carry out a representative case study to evaluate the performances of the three methods, focusing on economic efficiency, grid-safety, and computational load.