Smart prosumers with Distributed Generation (DGs) and controllable loads can provide cost-effective grid services. However, realizing this potential requires distributed optimization mechanisms that ensure market efficiency, participant privacy, and compliance with electricity market regulations. This paper presents a bi-level distributed optimization mechanism to maximize flexibility services from industrial parks and Internet Data Centers (IDCs) in distribution-level Congestion Management (CM) markets. The upper-level models the Distribution System Operator (DSO), which identifies congested lines using linear AC power flow analysis on pre-settled energy market results and sends corrective signals to prosumers. The lower level allows prosumers to adjust their operations accordingly and communicate updated transactions back to the DSO. A novel proxy-driven algorithm is proposed to facilitate service-sharing among geo-distributed IDCs, considering congestion issues. Additionally, an adaptive Alternating Direction Method of Multipliers (ADMM) algorithm enables decentralized coordination among market agents, achieving 74.52 % faster convergence than the standard ADMM. A real-world case study from Spain demonstrates that the proposed mechanism enables the grid operator to maximize grid services from prosumers, reducing congestion alleviation costs by 35.27 %. Moreover, IDCs reduced daily costs by 11.07 % through service-sharing and task-shifting aligned with CM market signals, while industrial parks achieved a 13.68 % cost reduction by aligning material production processes with CM market signals, both enabled by the proposed bi-level mechanism.

This article presents a four-level hierarchical model to incorporate decentralized energy communities (ECs) into local electricity markets. The model utilizes an innovative distribution system operator (DSO)-driven algorithm to maximize grid services from ECs, monetize their energy surplus, and adapt market exchanges to network security constraints. In level 1, EC members determine their internal scheduling and power exchanges. A decentralized peer-to-peer (P2P) structure embedded in level 1 enables power sharing with dynamic pricing and limited data sharing among EC members. Levels 2 and 3 involve the EC operators and the retailer company determining their market strategies. In level 4, a DSO-driven algorithm is deployed to evaluate security constraints and the feasibility of exchanges between market players. Implemented on a modified 594-node distribution network in Victoria, Australia, the model optimally integrates ECs with local electricity markets. By preserving agents' privacy and keeping exchange details confidential, the proposed model ensures next-day contracts adhere to network security restrictions, maximizes grid services from ECs, and reduces members' electricity bills by 7.5%.

The development of offshore transmission and wind power generation in the North Sea of Europe is advancing fast, but there are significant barriers to an integrated offshore grid in the region. This offshore grid is a multi-level, multi-actor system requiring a governance decision-making approach, but there is currently no proven governance framework for it, or for the expansion planning of the European power system in general. In addition, existing offshore expansion planning models do not endogenously include governance considerations, such as country vetoes to integrated lines. We develop a myopic Mixed-Integer Linear Programming model of offshore generation and transmission expansion planning to study the effect of integrated governance constraints. These constraints limit investments in integrated lines: non-conventional lines linking offshore wind farms to other countries or to other farms. Each constraint affects the system (including the main transmission corridors), transmission technologies and welfare distribution differently. We apply our model to a long-term case study of the 2030–2050 offshore expansion pathways using data from the e-Highway2050 project. Results confirm that the offshore grid is beneficial to society. Integrated governance constraints induce a modest loss of social welfare, but do not change significantly the existing welfare distribution asymmetry between countries and actor groups. They do strongly affect the interaction of line technologies and types (conventional or integrated), so the impact of the integrated governance constraints is more visible on the grid topology than on welfare levels and distribution. We highlight the need to consider technology and type interactions in expansion planning, especially between multiterminal HVDC and integrated transmission lines. Also, an offshore governance framework should address the use of multiterminal HVDC in a non-integrated grid, but this is a second-best option compared to an integrated grid.

In this paper, we propose a method to analyze the amount of operation reserves procured in a system based on two stages. The first stage is a detailed hourly unit commitment and the second stage is a simulation model with a shorter time period. The method is applied to the Spanish hydrothermal system and several cases are analyzed to determine the impact of the time period in the use of the operation reserves.

This paper proposes a power-based network-constrained unit commitment (UC) model as an alternative to the traditional deterministic UCs to deal with wind generation uncertainty. The formulation draws a clear distinction between power-capacity and ramp-capability reserves to deal with wind production uncertainty. These power and ramp requirements can be obtained from wind forecast information. The model is formulated as a power-based UC, which schedules power-trajectories instead of the traditional energy-blocks and takes into account the inherent startup and shutdown power trajectories of thermal units. These characteristics allow a correct representation of unit's ramp schedule which define their ramp availability for reserves. The proposed formulation significantly decreases operation costs compared to traditional deterministic and stochastic UC formulations while simultaneously lowering the computational burden. The operation cost comparison is made through 5-min economic dispatch simulation under hundreds of out-of-sample wind generation scenarios.

This paper presents a mixed-integer linear programming (MILP) reformulation of the thermal unit commitment (UC) problem. The proposed formulation is simultaneously tight and compact. The tighter characteristic reduces the search space and themore compact characteristic increases the searching speed with which solvers explore that reduced space. Therefore, as a natural consequence, the proposed formulation significantly reduces the computational burden in comparison with analogous MILP-based UC formulations. We provide computational results comparing the proposed formulation with two others which have been recognized as computationally efficient in the literature. The experiments were carried out on 40 different power system mixes and sizes, running from 28 to 1870 generating units.

This paper presents a mixed-integer linear programming (MILP) formulation of start-up (SU) and shut-down (SD) power trajectories of thermal units. Multiple SU power-trajectories and costs are modeled according to how long the unit has been offline. The proposed formulation significantly reduces the computational burden in comparison with others commonly found in the literature. This is because the formulation is 1) tighter, i.e., the relaxed solution is nearer to the optimal integer solution; and 2) more compact, i.e., it needs fewer constraints, variables and nonzero elements in the constraint matrix. For illustration, the self-unit commitment problem faced by a thermal unit is employed. We provide computational results comparing the proposed formulation with others found in the literature.

Reserves are playing each time a more important role due to the massive penetration of renewable energy sources nowadays. Operating reserves must be used for unforeseen events. All predictable events must be directly considered in the scheduling stage otherwise there will be an inefficient and unnecessary use of reserves that increases system operation costs and can even endanger the power system security. This paper presents a qualitative assessment of some widely used implicit assumptions in Unit Commitment (UC)-based Market-Clearing (MC) formulations. We show evidence of the impact on reserves and system security due to considering the use of energy blocks in the MC. In addition to this, we present the consequences on the reserve deployment due to the underlying accepted assumptions in UC-based MC formulations. Finally, we give some recommendations which must be incorporated in UC-based MC formulations in order to schedule and use the operating reserves efficiently.

This paper presents a short-term power system operation model where the electric vehicle management is being considered. Electric vehicles are considered as loads depending on the usage pattern. The operation model resorts to a mixed integer programming problem to determine the optimal system operation. We also evaluate vehicle to grid generation and power reserve service provision. Different EV share scenarios and V2G capabilities are evaluated for the mainland Spanish electric system. The contribution of electric vehicles on the integration of renewable sources is also determined.