Interconnection of transmission and distribution systems requires coordination among the operators of these systems. Such interconnections can provide resource efficiency and system security benefits by covering any generation shortfalls. Power flow coordination at the boundary nodes can enhance congestion and voltage management of the operators. Proposed distributed methods for coordination, as an alternative to centralised management, suffer from numerical instabilities or require large amounts of data exchange. To address these, this paper proposes a two-level decentralised Alternating Direction Method of Multipliers (ADMM). To validate its effectiveness and performance, the proposal is tested on an interconnected modified IEEE 118-bus transmission system and IEEE 33-bus distribution test systems.

Residential photovoltaic (PV) and battery based microgrids are regarded as potential enablers for decarbonisation. Such systems are considered to be effective means for enhancing self-consumption and reducing reliance on the grid. However, low-voltage distribution networks can be congested especially during peak solar generation hours. During such hours not only generation quantities, but also the amount of power transferred between the microgrid and the utility network can be uncertain. This paper proposes a Monte Carlo process-integrated self-adaptive memetic algorithm and a comprehensive model for optimising the microgrid design with the objective of minimising costs for households while considering the uncertainty of solar generation and grid constraints. Energy demand is partly flexible, and can be shifted to different hours. The proposed model determines the optimal number of solar panels and battery size. A non-linear battery degradation based on depth-of-discharge is considered. Proposed algorithm combines a conventional genetic algorithm (GA) with a hill-climbing technique to improve individual selection by updating crossover and mutation probabilities via a self-adaptive process. Constraint violations are handled through an adaptive penalty parameter update. The effectiveness of the algorithm is demonstrated in a case study, where the model accuracy and computational capability are compared against a standard GA.

Massive penetration of distributed energy resources (DERs) along with limitations of the network are increasingly causing congestion or voltage problems in transmission and distribution systems. The interconnectedness of these systems necessitates coordination amongst their operators to assure supply security. This paper proposes a methodology for coordination amongst a Transmission System Operator (TSO) and Distribution System Operators (DSOs) without any hierarchy imposed on the entities. Coordination enables optimal usage of flexibility from Active Distribution Systems (ADSs) by the TSO and mitigation of congestion and voltage problems. This work leverages a two-level Alternating Direction Method of Multipliers (ADMM), which guarantees convergence to an approximate stationary solution for multi-block problems involving multiple actors. Tested on a modified IEEE 118-bus transmission system coupled with IEEE 33-bus active distribution test systems, the benefits of the proposal are demonstrated.

Ambitious climate targets of the European Union to achieve a carbon-neutral Europe by 2050, along with an unprecedented energy crisis, are changing the nature of the energy system as well as diversifying energy generation sources. Within this context, distribution grids are undergoing fundamental changes to be able to efficiently host intermittent, weather-dependent, inverter-based renewable generation sources and emerging technologies such as battery energy storage systems. Matching demand and supply at local levels and democratization aspects of a new energy system where customers are empowered are a new paradigm. The operation of distribution systems with these aspects necessitates digitalization, large datasets generated with high time granularity, and advanced data analytics such as artificial intelligence (AI), so that the situational awareness of the grid is enhanced, and the safety of operations is maintained. This work outlines the main challenges of distribution system operations and presents current and potential future applications of big data and AI in this regard.

In pool-based electricity markets, generators may have incentives to deviate from market-clearing outcomes, due to non-convex costs resulting from their operational specifications. System operators compensate generators for those costs through uplift payments. However, uplift is not reflected in market prices, distorting price signals. Windfall profits by generators, as experienced due to high energy prices, triggered the consideration of a revenue cap to limit these profits. Also, system operators may need costly balancing when their decisions do not adequately account for adverse scenarios. This work proposes a risk-averse primal-dual pricing, co-optimising energy and reserves, explicitly ensuring cost recovery for generators and allowing for a revenue cap consideration. The devised model balances demand and supply at a least possible deviation from social welfare under adverse renewable generation scenarios. Out of a case study on a real system, it is shown that the proposed pricing schemes result in different scheduling outcomes. Consideration of a risk-averse decision framework leads to more alignment between schemes. A revenue cap brings about the least consumer expenses.

As weather-dependent renewables become the main source of generation in power systems, their intermittent supply nature requires system operators to account for uncertainty in market price formation and scheduling. In fact, renewable generation forecast errors cause high system-balancing costs. Conventional generators, on the other hand, have non-convex cost structures which incentivise misalignment with marginal pricing-based market outcomes. Out-of-market mechanisms, which blur price signals, are used to reimburse generators for their opportunity costs. Moreover, a surge in fuel prices may cause windfall profits for low-cost technologies and, to address this, the European Union recently considered introducing tailor-designed revenue caps. Leveraging on a risk-averse two-stage stochastic programming framework, this work enhances state-of-the-art models by providing a robust decision-making tool for system operators. Based on a primal–dual pricing, this tool endogenously balances energy and reserve scheduling related system costs with a stand-alone generator cost recovery under various schemes. The resulting market-clearing procedure is formulated as a Mixed Integer Bi-Linear Programming problem. A hybridisation of McCormick envelopes and binary expansion is proposed to enhance the computational capability to solve the problem. Three case studies illustrate the effectiveness of the proposed procedure.

Energy transition to decarbonise energy systems brings in new dynamics for distribution system operations. Increased amount of intermittent renewable generation by distributed energy resources causes some grid bottlenecks in the distribution systems. Power-to-Gas (P2G) and Gas-to-Power (G2P) technologies enable postponement of usage through a storage mechanism. When these technologies provide their flexibility services to power system Distribution System Operators, they need to adjust their trade in order to accommodate grid needs. This entails some lost opportunities for which the operators of these technologies may require compensation. One topic of interest is how to remunerate these technologies for their services. This paper evaluates the decision process of these technologies through an optimisation algorithm and analyses the flexibility provided by P2G and G2P hybrid operations in a case study. It is observed that these operations can be effective in resolving generation related bottlenecks technically. Additionally, opportunity costs for P2G-G2P operators are quantified.

Second-order cone programming (SOCP) is one of the common methods used to solve AC network-constrained unit commitment (UC) problems. The main shortcoming of this approach is the computational complexity increasing exponentially with the system size. Additionally, under high-system loading conditions, the results may not be exact. To address these issues, we propose a decomposition algorithm. First, we linearise quadratic relaxation for the UC conic formulation. Next, we apply an outer approximation algorithm that selects optimal cuts at each iteration. Numerical experiments show the effectiveness of the algorithm compared to the standard SOCP.