• Energy Research

Abstract Demand-side flexibility (DSF) has been touted as a possible solution to the challenges in power system operation arising from increasing intermittent renewables penetration and the emergence of electric vehicles. In Singapore, where around 24 to 60% of electricity demand in buildings could be attributed to heating, ventilation, and air conditioning (HVAC) purposes, air-conditioned buildings represent a significant flexibility resource which could be used to provide DSF and help tackle these challenges. This study aims to investigate the DSF potential of Singapore’s building stock and to explore how this potential could be realized through demand-side bidding. To this end, a building energy modeling tool with explicit modeling of the relationship between occupant comfort and HVAC load with model predictive control, CoBMo, is used. CoBMo allows optimal load scheduling to be expressed as a linear programming problem: minimizing overall electricity cost while maintaining occupant comfort. A price-based market clearing model is developed to evaluate demand-side bidding implementation, for which a case study on Singapore’s Downtown Core district is developed. Three scenarios with possible future utility-scale photovoltaic (PV) penetration in Singapore’s electricity system are explored, alongside a sensitivity analysis and a comparison between centralized dispatch and demand-side bidding with price-quantity pairs and linear curves. Results of the analysis show that DSF potential varies between building types, depending on cooling load and occupancy schedule. When extreme price fluctuations happen in future Singapore electricity market with 10 GWp PV penetration, demand-side bidding could aid consumers to utilize their DSF potential by encouraging more effective energy use and in turn, reducing their total electricity cost.

Abstract The planning of electric distribution grids aims at designing the most cost-efficient grid topology, while ensuring sufficient maximum capacity in the case of peak load conditions. With the advent of demand-side flexibility, there is the opportunity to reshape peak loads such that the investment cost of the electric grid decreases, in exchange for a minor increase in the operating cost. To this end, there exists a gap in formulating the trade-off between investment cost and operating cost, and a unsatisfactory understanding of the potential cost savings. This paper formulates a numerical optimization problem for the planning of the electric distribution grid, which incorporates the demand-side flexibility from thermal building systems, e.g., heating, ventilation and air-conditioning systems. The problem is formulated as a single-stage, mixed-integer quadratic program and aims at minimizing the investment cost for the grid along with the operating cost of the flexible loads. This is subject to the fixed electricity demand and thermal-comfort constraints of building occupants. The approach is tested on a district planning test case based in Singapore, where the results show reductions of up to 36.3% in investment cost and reductions of up to 0.81% in total annualized cost. Urban planning authorities, developers and utility companies can all benefit from the presented approach to make optimized investment decisions. For building operators, the results point to the need to adopt control systems for demand-side flexibility.

In the distribution grid, flexible resources are becoming an important tool to address challenges from uncertain renewable generation and increasing peak loads. To this end, there exists a need for a dedicated software framework to simulate the active distribution grid operation with flexible resources. The Flexible Distribution Grid Demonstrator (FLEDGE) is presented as a simulation tool which integrates active distribution grid operation with classic power flow studies. This paper outlines the requirements, software architecture and key capabilities of FLEDGE.

The increasing penetration of active elements at lower voltage levels introduces new challenges for efficient and reliable operation of distribution grids. Reverse power flow and the addition of interconnections between feeders not only make relay coordination harder, it requires development and testing of new control algorithms to ensure voltages are kept withing safe operating limits. Simulation tools that provide a good compromise between detail and computational complexity are necessary. This paper summarizes the requirements and characteristics for operating and simulating future smart distribution grids. Special focus is put on existing simulation tools that allow testing of complex algorithms involving a combination of energy sources and loads, i.e., distributed generation (DG), flexible load (FL) and energy storage. Finally, the importance of co-simulation of power and communications systems is discussed.

This paper describes the PRIMO project which aims at defining, modeling and simulating an operation platform that facilitates market participation of interconnected microgrids (MGs) in the distribution grid market. High penetration of distributed energy resources (DERs) not only increases the complexity of distribution grid operations but can also bring new services and value streams. To this end, interconnected MGs to provide coordination between DERs and distribution grid brings key benefits to improve the social welfare efficiency. The proposed platform comprises physical system modelling tools, and communication interfaces between two hierarchical levels comprising i) distribution grid-to-MG, ii) MG-to-MG energy/flexibility exchange, aiding in the result interpretation and practical realization. Leveraging from the decentralized organization, the operational autonomy and information privacy of each MG is protected.

This article presents a case study of distributed generation and flexibility potential for a multienergy system in an urban district in Singapore. The analysis incorporates real-life data of a local energy system consisting of flexible loads (i.e., district cooling demand from air-conditioned buildings) and distributed generators (DGs) (i.e., waste-to-energy (W2E) generators and photovoltaic (PV) generators) from a representative study area. The demand-side flexibility (DSF) potentials from air-conditioned buildings are derived based on a state-space model and its underlying base load estimation. Besides the conventional consideration of PV system integration in the urban environment, we conducted a feasibility study of the distributed W2E technology deployment and estimated the generation potentials for the study area. Furthermore, to facilitate flexibility and energy exchange, market frameworks are proposed to harvest energy and flexibility from distributed energy resources (DERs) and in the real-time market context in Singapore.

Flexible electric load control is an important tool for the future power system in the face of recent challenges to the electric grid. This paper presents a control framework for combining optimal load scheduling for flexible loads, a dualised worst-case formulation of the uncertain disturbance prediction errors and reserve scheduling via a dualised worst-case formulation with a distributed method for congestion management in the distribution system via a subgradient algorithm. The suitability of the developed framework is demonstrated on a benchmark distribution system.

A novel peer-to-peer (P2P) market design is proposed in this work for the distribution grid level. Envisioning that the grid constraints violations are the major challenge for P2P energy sharing, we propose these to be handled through the ancillary service (AS) market. By calculating the decomposable distribution locational marginal prices (DLMPs), the essential price signals of procuring ASs can be recovered to determine the grid usage prices (GUPs) to each P2P transaction. Hence, the GUPs, due to their decomposable properties, act as incentive signals for the P2P market to support the grid operation in terms of loss reduction, voltage support and congestion management. The proposed market design comprises i) an interactive market design of P2P trade & AS and, ii) a fully distributed peer-centric market-clearing model for P2P energy trade. The duality analysis provides the composition of market equilibrium prices of P2P trading and their interpretations. The case studies demonstrate the effectiveness of the proposed P2P trade to support grid operational objectives.

Distribution locational marginal prices (DLMPs) have been formulated for electric distribution grids to economically dispatch distributed energy resources (DERs) while addressing operational constraints of the electric grid. This paper proposes to extend this methodology to thermal grids, i.e, district heating or cooling systems, and specifically the combined operation of thermal and electric grids. To this end, thermal and electric grid models are formulated in a linear approximate model fashion. Then, the derivation and decomposition of DLMPs is formulated based on the combined optimal operation problem, assuming a linear state space model form for flexible loads (FLs). The ability of the DLMPs to reflect operational constraints in the thermal grid is demonstrated for a test case with 22 FLs.