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More customers are tending to install batteries with photovoltaic (PV), so they can better control their electricity bills. In this context, customers may be tempted to go off-grid at a substantial up-front cost, leading electricity companies into a death spiral, thereby raising electricity price further on those remaining on grid. Neighborhood energy markets can promote the sharing of locally generated renewable energy and encourage prosumers to stay on grid with financial incentives. A novel neighborhood energy trading (NET) mechanism is developed using the topology of existing radial distribution network to encourage sustainable energy sharing in neighborhood and encourage prosumers to stay on grid. This mechanism considers loss, congestion management, and voltage regulation, and it is scalable with low computation and communication overhead. An IEEE test system is used to validate the NET mechanism. The simulation shows that the price and flow results are obtained with fast computation speed (within 10 iterations) and with loss reflected, flow limit reinforced, and voltage regulated. This study proves that the economic demand-supply-based pricing mechanism can be applied effectively in distribution networks to help encourage more renewable energy sharing in sustainable neighborhood and avoid energy network death spiral.

Solar district heating systems have shown significant promise to facilitate the large scale adoption of solar energy technologies and thus substantially reduce greenhouse gas emissions. Given the mismatch between solar energy and district heating demand, energy storage devices play a critical role given their capacity to stockpile solar energy in both the short-term (hours to days) and long-term (months). However, the integration, sizing and control of energy storage technologies is far from simple. This paper investigates sizing and controlling thermal energy storage from the perspective of its performance within a district heating system, highlighting the close link between design and control. A 52-house Canadian solar district heating system, the Drake Landing Solar Community (DLSC), was used as a case study. This system uses solar collectors as main energy supply, borehole thermal energy storage (BTES) for seasonal storage and two 120-m3 water tanks for short-term thermal storage (STTS).The effect of (a) storage sizing (STTS volume) and (b) storage control (rate at which energy is either injected or extracted from the BTES) was evaluated. A control-oriented model, calibrated and validated with operational data at 10-min intervals, was used along with an optimal rule-based control to gauge system primary energy use. Different scenarios were tested, with STTS volumes ranging from 120 m3 to 480 m3, and BTES loop nominal flow rates between 2.5 and 4.5 L/s. An optimization routine was developed to calculate the optimum parameters of the rule-based control strategy. Results show that, in comparison with the design and control in place, primary energy savings of 13%–30% (with BTES flow rates of 2.5–4.5 L/s) could have been obtained with the proposed rule-based control strategy. By decreasing the STTS volume to 120-m3, energy savings up to 6% could still be achieved; savings could reach 27%–36% by increasing the STTS size to 360-m3.

• Optimal use of short- and long-term thermal energy storage devices is investigated. • A novel strategy for controlling variable speed pump flow rates is presented. • Pump electrical energy savings over 40% are obtained. • Energy costs and GHG emissions are reduced by 34% and 29%, respectively. Solar district heating systems are a promising solution to facilitate the adoption of large-scale, solar energy-based technologies. Thermal energy storage devices could also play an instrumental role to manage the mismatch between solar energy resources and community heating loads; however, controlling such systems is no easy task. In this paper, a novel heuristic reactive control strategy is developed to properly manage the thermal energy storage capacity, at both the short-term and the long-term, in a Canadian solar district heating system (the Drake Landing Solar Community). This strategy targets the optimization of: (a) the temperature difference across solar collectors and (b) the flow rate modulation of the variable speed pump linking the short-term and the seasonal thermal energy storage devices. A simulation is used to compare this new control approach with the conventional control strategy currently in place. Results are assessed for the year 2017–18 in terms of energy, cost and greenhouse gas emissions. Despite a 10% increase in natural gas consumption, electricity use was decreased by 43%; reductions of 34% and 29% were respectively achieved in terms of energy cost and GHG emissions. [ABSTRACT FROM AUTHOR]

The development of a control-oriented model of a solar community with seasonal storage (the Drake Landing Solar Community) is investigated. The proposed approach, intended to facilitate the development and testing of control strategies and targeting an actual predictive control implementation, is based on grey-box models, and enables the prediction of the system state (temperatures at key locations). This paper discusses the concept of state update procedure (whereby the system state is periodically corrected with measurements), which plays a fundamental role for control purposes. Firstly, the DLSC is presented and both operation and monitoring system are described. Secondly, a simplified model is developed for each sub-system: district and solar loops, short-term (water tanks) and seasonal (borehole) thermal energy storage, and existing operation rules are encoded. Finally, the model is calibrated and validated by using measurements at 10-min intervals over two years of operation (2015–2016, 2016–2017) and accurately predicts the system performance. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • The economic and environmental impact of solar district heating systems is studied. • Multi-objective optimization is carried out in a wide range of EU climate zones. • A solar fraction above 90% can be achieved for all proposed EU climate zones. • High environmental performance is realized in solar district heating systems. • The future fuel prices growth supports the solar district heating economically. Abstract Aligning with the ambitious EU 2030 climate and energy package for cutting the greenhouse emissions and replacing conventional heat sources through the presence of renewable energy share inside efficient district heating fields, central solar heating plants coupled with seasonal storage (CSHPSS) can have a viable contribution to this goal. However, the technical performance variation combined with inadequate financial assessment and insufficient environmental impact data associated with the deployment of those innovative district heating systems represents a big challenge for the broad implementation of CSHPSS in Europe. In this context, our paper presents a comprehensive evaluation for the possibility of integrating CSHPSS in the residential sector in various EU member states through the formulation of a multi-objective optimization framework. This framework comprises the life cycle cost analysis for the economic evaluation and the life cycle assessment for the environmental impact estimation simultaneously. The technical performance is also considered by satisfying both the space heating demand and the domestic hot water services. The methodological framework is applied to a residential neighborhood community of 1120 apartments in various EU climate zones with Madrid, Athens, Berlin, and Helsinki acting as a proxy for the Mediterranean continental, Mediterranean, central European, and Nordic climates, respectively. The optimization results regarding the energy performance show that the CSHPSS can achieve a renewable energy fraction above 90% for the investigated climate zones. At the same time, the environmental assessment shows significant improvement when using the CSHPSS in comparison to a natural gas heating system, in those cases the environmental impact is reduced up to 82.1–86.5%. On the other hand, substantial economic improvement is limited, especially in the Mediterranean climate zone (Athens) due to low heating demands and the prices of the non-renewable resources. There the total economic cost of the CSHPSS plants can increase up to 50.8% compared to a natural gas heating system. However, considering the incremental tendency in natural gas prices all over EU nowadays, the study of future plant costs confirms its favorable long-term economic feasibility. [ABSTRACT FROM AUTHOR]

Highlights • Centralized and semi-decentralized solar district heating systems are studied. • Multi-objective optimization is carried out for both the systems and compared. • High performance in decentralized system is realized at 35% lower lifecycle cost. • The centralization of domestic hot water network increased the losses by 40–12%. • Collector area vary to 5400 m2 in centralized and 3000 m2 in decentralized system. Abstract Solar thermal energy is widely recognized as one of the most important renewable energy resources. However, in high latitudes, due to various climatic and mismatch challenges, such solar district heating networks are difficult to implement. The objective of the paper is to optimize and compare two different design layouts and control strategies for solar district heating systems in Finnish conditions. The two different designs proposed are a centralized and a semi-decentralized solar district heating system. The centralized system consists of two centralized short-term tanks operating at different temperature levels charged by a solar collector and heat pumps. Borehole thermal energy storage is also charged via these two centralized tanks. In contrast, the semi-decentralized system consists of one centralized low temperature tank charged by a solar collector and a borehole thermal energy storage and decentralized high temperature tank charged by an individual heat pump in each house. In this case, borehole thermal energy storage is charged only by the centralized warm tank. These systems are designed using the dynamic simulation software TRNSYS for Finnish conditions. Later on, multi-objective optimization is carried out with a genetic algorithm using the MOBO (Multi-objective building optimizer) optimization tool, where two objectives, i.e. purchased electricity and life cycle costs, are minimized. Various design variables are considered, which included both component sizes and control parameters as inputs to the optimization. The optimization results show that in terms of life cycle cost and purchased electricity, the decentralized system clearly outperforms the centralized system. With a similar energy performance, the reduction in life cycle cost is up to 35% for the decentralized system. Both systems can achieve close to 90% renewable energy fraction. These systems are also sensitive to the prices. Furthermore, the results show that the solar thermal collector area and seasonal storage volume can be reduced in a decentralized system to reduce the cost compared to a centralized system. The losses in the centralized system are 40–12% higher compared to the decentralized system. The results also show that in both systems, high performance is achieved when the borehole storage is wider with less depth, as it allows better direct utilization of seasonally stored heat. The system layout and controls varied the performance and life cycle cost; therefore it is essential to consider these when implementing such systems. [ABSTRACT FROM AUTHOR]

Abstract Short-term forecasting of thermal energy demand is critical to optimally manage on-site renewable energy generation and the charge and discharge of energy storage devices in district heating and cooling (DHC) systems. As part of a larger study on advanced predictive control for a solar district heating system with 52 homes – the Drake Landing Solar Community (DLSC) – this paper investigates the use of Machine Learning algorithms to predict the aggregated heating load of the community. In this study, the initial approach to estimate the heating load of the DLSC employed a piecewise linear regression based on the outdoor air temperature. Such an approach yields significant errors, in particular when weather forecasts are used instead of actual outdoor air temperature measurements. It has been found that Machine Learning algorithms, such as decision trees, can significantly improve the accuracy of predicted heating loads by incorporating the effect of additional influencing factors (e.g., time of the day, day of the week, solar radiation, etc.). In this study, the predicted heating demand obtained from different algorithms are compared under two different scenarios; (a) by using actual weather conditions from measured data; (b) by using weather forecasts. The potential implementation of such models for control purposes is discussed. [ABSTRACT FROM AUTHOR]

There is a substantial need to accelerate the advancement and implementation of clean energy technologies in order to solve the challenges of the energy crisis and climate change. Solar heating technology is a feasible solution among clean energy technologies. In real conditions such complex systems often suffer from different kinds of technical failures and deviations reducing the system performance. This paper focuses on the challenges of a solar district heating system at high latitudes, proposes an optimized solution and investigates the influence of possible failures in planning, implementation and operation phase. The configuration proposed is a heat pump connected between two tanks, using solar-charged borehole storage to directly charge the lower temperature tank. Dynamic simulations were performed and a multi-objective optimization was carried out. The impact of the considered system solutions on the renewable energy fraction, purchased electricity and investment cost as a function of demand, solar thermal and photovoltaic areas, tanks and borehole volumes have been evaluated. The influence of 10 different technical failures was investigated. The study showed that in the optimized system, the most serious faults were i) de-stratification of the storage tanks (23–35% increase in annual purchased electricity) ii) on-off instead of variable speed control of the solar circulation pump (1–22% increase) and iii) reduction in heat pump performance (7–21%). These numbers of course depend on the initial assumptions, but still they show the magnitude of performance reduction some failures can achieve. Therefore, these parameters need to be considered during the implementation of such a system. [ABSTRACT FROM AUTHOR]

This paper proposes various community-sized solar heating systems configurations for cold climate. Three configurations were proposed, (I)a heat pump connected to two tanks in parallel, using charged borehole storage, (II)a heat pump connected between two tanks, using charged borehole storage to directly charge the lower temperature tank, and (III)two heat pumps used in series, one between the tanks and the other between the lower temperature tank and ground. In configurations (I) and (II) the vertical borehole field is used as a seasonal storage, in (III) it is used to extract heat only. The studied energy flows are heat and electricity. The border consists of energy production systems, heating grid and buildings. The impact of the considered system solutions on the heating renewable energy fraction, on-site electrical energy fraction, purchased energy and full cost as a function of the demand, solar thermal and photovoltaic areas, tanks and borehole volumes has been evaluated. The dynamic simulations results shows that an average renewable energy fraction of 53–81% can be achieved, depending upon the energy systems’ configuration. Furthermore, Energy System II utilizes less energy compared to other systems. In all three systems medium-sized solar thermal area is more beneficial instead of large area. [ABSTRACT FROM AUTHOR]

A solar community of 100 residential houses was optimized for Finnish conditions with the aim of achieving a 90% solar fraction for both space heating and domestic hot water. Optimization was done using a novel method based on neural network metamodelling and compared to the standard NSGA-II genetic algorithm. Compared to NSGA-II, the new method obtained a larger hypervolume by finding better solutions both in the center and edge of the non-dominated front. The combined non-dominated front of both methods was better than either one separately. The performance target was achieved as the optimal solar community designs had heating solar fractions ranging from 64% to 95%. [ABSTRACT FROM AUTHOR]

The presented paper focuses on the design of different control strategies of a cold climate community-size solar thermal system located in Finland. The system was designed on TRNSYS software in order to perform dynamic simulation. A solar thermal system operating with various control strategies has been designed with an integrated ground source heat pump and a seasonal borehole storage to provide domestic hot water (DHW) and space heating (SH) for community-size demand. The system has two short term storage tanks, a hot tank and a warm tank. The impact of the considered system solutions on electricity consumption has been evaluated and compared as a function of the different collector control modes and different tank configurations (short term tanks sizes). Results have shown that the proposed system was able to provide a 78-83% renewable energy fraction. Total electricity consumption of the heating system varied by 20% between the best and the worst cases. Furthermore, system performance was better when solar energy was mainly stored in the warm tank. During a 5-year simulation, the annual seasonal storage efficiency improved from 0.23 to 0.31, whereas the heat pump electricity consumption reduced from 57.17 MWh to 45.93 MWh. The demand in winter was met mainly through ground heat and the rest was provided by the heat pump compressor. However, the demand in summer was met almost completely by solar energy. [ABSTRACT FROM AUTHOR]

• Primary energy use is minimized in a district heating network with seasonal storage. • Predictive control formulation was applied to optimize variable speed pump use. • Pumping power savings of 47% are obtained. • Energy costs and greenhouse gas emissions are reduced by 38% and 32%, respectively. This paper investigates the development and assessment of a model-based predictive control strategy for the district heating system at the Drake Landing Solar Community (DLSC), in Okotoks (Alberta, Canada). Thermal energy is collected by solar thermal collectors and stored seasonally by means of a borehole field. Two water tanks are used as short-term storage, acting as a central unit connecting solar collectors, long-term storage and a district loop. The DLSC has succeeded in using solar energy collected during the summer to provide nearly all the heating needs of this 52-home community in winter, with solar fractions consistently over 90%. The proposed predictive control strategy aims to minimize primary energy consumption while maintaining the same solar fraction. This simulation study –based on model calibrated with on-site measurements– focuses on the optimization of circulation pump speed to manage energy exchange between long-term and short-term storage systems. Minimizing pumping electricity use is a critical aspect of the community environmental impact, since fossil-fuel thermal plants are prevailing in Alberta. Simulation results indicate that the proposed strategy would save, on an annual basis, about 47% of total pump electricity use. This would result in savings in terms of cost (38%) and greenhouse gas emissions (32%). [ABSTRACT FROM AUTHOR]